html_url stringlengths 51 51 | comments stringlengths 67 24.7k | title stringlengths 6 280 | body stringlengths 51 36.2k | comment_length int64 16 1.45k | text stringlengths 190 38.3k | embeddings list |
|---|---|---|---|---|---|---|
https://github.com/huggingface/datasets/issues/2434 | this is also the case for the following datasets and configurations:
* `mlqa` with config `mlqa-translate-train.ar`
| Extend QuestionAnsweringExtractive template to handle nested columns | Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support "flat" features. We should extend the functionality to cover QA datasets like:
* `iapp_wiki_qa_squad`
* `parsinlu_reading_comprehension`
where the nested features differ with those from `squad` and trigger an `ArrowNot... | 16 | Extend QuestionAnsweringExtractive template to handle nested columns
Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support "flat" features. We should extend the functionality to cover QA datasets like:
* `iapp_wiki_qa_squad`
* `parsinlu_reading_comprehension`
where the ... | [
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https://github.com/huggingface/datasets/issues/2434 | The current task API is somewhat deprecated (we plan to align it with `train eval index` at some point), so I think we can close this issue. | Extend QuestionAnsweringExtractive template to handle nested columns | Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support "flat" features. We should extend the functionality to cover QA datasets like:
* `iapp_wiki_qa_squad`
* `parsinlu_reading_comprehension`
where the nested features differ with those from `squad` and trigger an `ArrowNot... | 27 | Extend QuestionAnsweringExtractive template to handle nested columns
Currently the `QuestionAnsweringExtractive` task template and `preprare_for_task` only support "flat" features. We should extend the functionality to cover QA datasets like:
* `iapp_wiki_qa_squad`
* `parsinlu_reading_comprehension`
where the ... | [
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https://github.com/huggingface/datasets/issues/2431 | Thanks for reporting !
#2433 fixed the issue, thanks @mariosasko :)
We'll do a patch release soon of the library.
In the meantime, you can use the fixed version of adversarial_qa by adding `script_version="master"` in `load_dataset` | DuplicatedKeysError when trying to load adversarial_qa | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
dataset = load_dataset('adversarial_qa', 'adversarialQA')
```
## Expected results
The dataset should be loaded into memory
## Actual results
>DuplicatedKeysError: FAILURE TO GENERATE DATASET ... | 36 | DuplicatedKeysError when trying to load adversarial_qa
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
dataset = load_dataset('adversarial_qa', 'adversarialQA')
```
## Expected results
The dataset should be loaded into memory
## Actual resu... | [
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https://github.com/huggingface/datasets/issues/2426 | It should probably work out of the box to save structured data. If you want to show an example we can help you. | Saving Graph/Structured Data in Datasets | Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data ty... | 23 | Saving Graph/Structured Data in Datasets
Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python... | [
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https://github.com/huggingface/datasets/issues/2426 | An example of a toy dataset is like:
```json
[
{
"name": "mike",
"friends": [
"tom",
"lily"
],
"articles": [
{
"title": "aaaaa",
"reader": [
"tom",
"lucy"
... | Saving Graph/Structured Data in Datasets | Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data ty... | 131 | Saving Graph/Structured Data in Datasets
Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python... | [
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0.588275134563... |
https://github.com/huggingface/datasets/issues/2426 | Hi,
you can do the following to load this data into a `Dataset`:
```python
from datasets import Dataset
examples = [
{
"name": "mike",
"friends": [
"tom",
"lily"
],
"articles": [
{
"title": "aaaaa",
"... | Saving Graph/Structured Data in Datasets | Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data ty... | 93 | Saving Graph/Structured Data in Datasets
Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python... | [
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https://github.com/huggingface/datasets/issues/2426 | Thank you so much, and that works! I also have a question that if the dataset is very large, that cannot be loaded into the memory. How to create the Dataset? | Saving Graph/Structured Data in Datasets | Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data ty... | 31 | Saving Graph/Structured Data in Datasets
Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python... | [
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https://github.com/huggingface/datasets/issues/2426 | If your dataset doesn't fit in memory, store it in a local file and load it from there. Check out [this chapter](https://huggingface.co/docs/datasets/master/loading_datasets.html#from-local-files) in the docs for more info. | Saving Graph/Structured Data in Datasets | Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python value type when inferring an Arrow data ty... | 28 | Saving Graph/Structured Data in Datasets
Thanks for this amazing library! And my question is I have structured data that is organized with a graph. For example, a dataset with users' friendship relations and user's articles. When I try to save a python dict in the dataset, an error occurred ``did not recognize Python... | [
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0.7541983127... |
https://github.com/huggingface/datasets/issues/2424 | Hi,
`load_dataset` returns an instance of `DatasetDict` if `split` is not specified, so instead of `Dataset.load_from_disk`, use `DatasetDict.load_from_disk` to load the dataset from disk. | load_from_disk and save_to_disk are not compatible with each other | ## Describe the bug
load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split
## Steps to reproduce the bug
... | 24 | load_from_disk and save_to_disk are not compatible with each other
## Describe the bug
load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the loa... | [
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0.3753903806209564,
0.0848359614610672,
0.155403867363929... |
https://github.com/huggingface/datasets/issues/2424 | Though I see a stream of issues open by people lost between datasets and datasets dicts so maybe there is here something that could be better in terms of UX. Could be better error handling or something else smarter to even avoid said errors but maybe we should think about this. Reopening to use this issue as a discussi... | load_from_disk and save_to_disk are not compatible with each other | ## Describe the bug
load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split
## Steps to reproduce the bug
... | 73 | load_from_disk and save_to_disk are not compatible with each other
## Describe the bug
load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the loa... | [
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0.34048062562942505,
0.07601669430732727,
0.185123354196548... |
https://github.com/huggingface/datasets/issues/2424 | We should probably improve the error message indeed.
Also note that there exists a function `load_from_disk` that can load a Dataset or a DatasetDict. Under the hood it calls either `Dataset.load_from_disk` or `DatasetDict.load_from_disk`:
```python
from datasets import load_from_disk
dataset_dict = load_fr... | load_from_disk and save_to_disk are not compatible with each other | ## Describe the bug
load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the load_from_disk only works on one split
## Steps to reproduce the bug
... | 45 | load_from_disk and save_to_disk are not compatible with each other
## Describe the bug
load_from_disk and save_to_disk are not compatible. When I use save_to_disk to save a dataset to disk it works perfectly but given the same directory load_from_disk throws an error that it can't find state.json. looks like the loa... | [
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0.35007691383361816,
0.06756189465522766,
0.123415008187294,
-... |
https://github.com/huggingface/datasets/issues/2415 | It actually seems to happen all the time in above configuration:
* the function `filter_by_duration` correctly loads cached processed dataset
* the function `prepare_dataset` is always reexecuted
I end up solving the issue by saving to disk my dataset at the end but I'm still wondering if it's a bug or limitation ... | Cached dataset not loaded | ## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def filter_by_duration(batch):
... | 53 | Cached dataset not loaded
## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def... | [
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0.18866592645645142,
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https://github.com/huggingface/datasets/issues/2415 | Hi ! The hash used for caching `map` results is the fingerprint of the resulting dataset. It is computed using three things:
- the old fingerprint of the dataset
- the hash of the function
- the hash of the other parameters passed to `map`
You can compute the hash of your function (or any python object) with
```... | Cached dataset not loaded | ## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def filter_by_duration(batch):
... | 94 | Cached dataset not loaded
## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def... | [
-0.29785436391830444,
-0.04235464334487915,
-0.07583025097846985,
0.42361658811569214,
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0.2326418161392212,
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0.04105199873447418,
0.18866592645645142,
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-0.225290566... |
https://github.com/huggingface/datasets/issues/2415 | > If `prepare_dataset` is always executed, maybe this is because your `processor` has a different hash each time you want to execute it.
Yes I think that was the issue.
For the hash of the function:
* does it consider just the name or the actual code of the function
* does it consider variables that are not pas... | Cached dataset not loaded | ## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def filter_by_duration(batch):
... | 70 | Cached dataset not loaded
## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def... | [
-0.29785436391830444,
-0.04235464334487915,
-0.07583025097846985,
0.42361658811569214,
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0.18866592645645142,
-0.2849697470664978,
-0.225290566... |
https://github.com/huggingface/datasets/issues/2415 | > does it consider just the name or the actual code of the function
It looks at the name and the actual code and all variables such as recursively. It uses `dill` to do so, which is based on `pickle`.
Basically the hash is computed using the pickle bytes of your function (computed using `dill` to support most pytho... | Cached dataset not loaded | ## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def filter_by_duration(batch):
... | 87 | Cached dataset not loaded
## Describe the bug
I have a large dataset (common_voice, english) where I use several map and filter functions.
Sometimes my cached datasets after specific functions are not loaded.
I always use the same arguments, same functions, no seed…
## Steps to reproduce the bug
```python
def... | [
-0.29785436391830444,
-0.04235464334487915,
-0.07583025097846985,
0.42361658811569214,
-0.07194630056619644,
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0.2326418161392212,
0.3540043532848358,
0.20896084606647491,
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0.04105199873447418,
0.18866592645645142,
-0.2849697470664978,
-0.225290566... |
https://github.com/huggingface/datasets/issues/2413 | Hi ! Can you try using a more up-to-date version ? We added the task_templates in `datasets` 1.7.0.
Ideally when you're working on new datasets, you should install and use the local version of your fork of `datasets`. Here I think you tried to run the 1.7.0 tests with the 1.6.2 code | AttributeError: 'DatasetInfo' object has no attribute 'task_templates' | ## Describe the bug
Hello,
I'm trying to add dataset and contribute, but test keep fail with below cli.
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<my_dataset>`
## Steps to reproduce the bug
It seems like a bug when I see an error with the existing dataset,... | 52 | AttributeError: 'DatasetInfo' object has no attribute 'task_templates'
## Describe the bug
Hello,
I'm trying to add dataset and contribute, but test keep fail with below cli.
` RUN_SLOW=1 pytest tests/test_dataset_common.py::LocalDatasetTest::test_load_dataset_all_configs_<my_dataset>`
## Steps to reproduce th... | [
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0.26718196272850037,
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0.2545938491821289,
0.3851536810398102,
0.03191317617893219,
0.18610899150371552,
0.09519115090370178,
0.44205597043037415,
-0.15097461640834808,
0.003988794982... |
https://github.com/huggingface/datasets/issues/2407 | Hi @cindyxinyiwang,
Did you try adding `.arrow` after `cache_file_name` argument? Here I think they're expecting something like that only for a cache file:
https://github.com/huggingface/datasets/blob/e08362256fb157c0b3038437fc0d7a0bbb50de5c/src/datasets/arrow_dataset.py#L1556-L1558 | .map() function got an unexpected keyword argument 'cache_file_name' | ## Describe the bug
I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that ".map() function got an unexpected... | 24 | .map() function got an unexpected keyword argument 'cache_file_name'
## Describe the bug
I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map... | [
0.1322997808456421,
0.06898844242095947,
0.03823639452457428,
0.000056201592087745667,
0.05203055590391159,
0.2963717579841614,
0.1708022654056549,
0.3389787971973419,
-0.0635625422000885,
-0.020300745964050293,
0.1289217174053192,
0.6100242137908936,
-0.24109242856502533,
-0.5483767390251... |
https://github.com/huggingface/datasets/issues/2407 | Hi ! `cache_file_name` is an argument of the `Dataset.map` method. Can you check that your `dataset` is indeed a `Dataset` object ?
If you loaded several splits, then it would actually be a `DatasetDict` (one dataset per split, in a dictionary).
In this case, since there are several datasets in the dict, the `Datas... | .map() function got an unexpected keyword argument 'cache_file_name' | ## Describe the bug
I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that ".map() function got an unexpected... | 72 | .map() function got an unexpected keyword argument 'cache_file_name'
## Describe the bug
I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map... | [
0.0843966156244278,
0.09359604120254517,
0.045661598443984985,
0.01830264925956726,
0.041557565331459045,
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0.2214866578578949,
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0.6276782155036926,
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https://github.com/huggingface/datasets/issues/2407 | I think you are right. I used cache_file_names={data1: name1, data2: name2} and it works. Thank you! | .map() function got an unexpected keyword argument 'cache_file_name' | ## Describe the bug
I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map() function, it throws an error that ".map() function got an unexpected... | 16 | .map() function got an unexpected keyword argument 'cache_file_name'
## Describe the bug
I'm trying to save the result of datasets.map() to a specific file, so that I can easily share it among multiple computers without reprocessing the dataset. However, when I try to pass an argument 'cache_file_name' to the .map... | [
0.15060636401176453,
0.09318815171718597,
0.04623489826917648,
-0.006893312558531761,
0.04963522031903267,
0.2938353419303894,
0.2111460268497467,
0.35865527391433716,
-0.0644398033618927,
0.017635982483625412,
0.119480200111866,
0.6066251397132874,
-0.2475740611553192,
-0.4879624843597412... |
https://github.com/huggingface/datasets/issues/2400 | Hi,
did you fill out the env info section manually or by copy-pasting the output of the `datasets-cli env` command?
This code should work without issues on 1.6.2 version (I'm working on master (1.6.2.dev0 version) and can't reproduce this error). | Concatenate several datasets with removed columns is not working. | ## Describe the bug
You can't concatenate datasets when you removed columns before.
## Steps to reproduce the bug
```python
from datasets import load_dataset, concatenate_datasets
wikiann= load_dataset("wikiann","en")
wikiann["train"] = wikiann["train"].remove_columns(["langs","spans"])
wikiann["test"] =... | 40 | Concatenate several datasets with removed columns is not working.
## Describe the bug
You can't concatenate datasets when you removed columns before.
## Steps to reproduce the bug
```python
from datasets import load_dataset, concatenate_datasets
wikiann= load_dataset("wikiann","en")
wikiann["train"] = w... | [
-0.05039629340171814,
-0.16309911012649536,
-0.03724938631057739,
0.2921578586101532,
0.07128670066595078,
0.1608157753944397,
0.42755746841430664,
0.3972991108894348,
-0.12802371382713318,
0.09719806164503098,
-0.2116852104663849,
0.28301164507865906,
0.11641468107700348,
0.24014712870121... |
https://github.com/huggingface/datasets/issues/2396 | Hi ! Thanks for reporting
cc @pjox is this an issue from the data ?
Anyway we should at least mention that OSCAR could contain such contents in the dataset card, you're totally right @jerryIsHere | strange datasets from OSCAR corpus | 
From the [official site ](https://oscar-corpus.com/), the Yue Chinese dataset should have 2.2K... | 35 | strange datasets from OSCAR corpus
From the [official site ](https://oscar-corpus.com/), the... | [
0.26769956946372986,
0.05709494650363922,
-0.0023011192679405212,
0.5770171880722046,
0.15650038421154022,
0.06097047030925751,
-0.012423692271113396,
0.24926769733428955,
-0.26459363102912903,
0.048613112419843674,
-0.5796424746513367,
-0.1079980880022049,
0.04310471937060356,
-0.05117529... |
https://github.com/huggingface/datasets/issues/2396 | Hi @jerryIsHere , sorry for the late response! Sadly this is normal, the problem comes form fasttext's classifier which we used to create the original corpus. In general the classifier is not really capable of properly recognizing Yue Chineese so the file ends un being just noise from Common Crawl. Some of these proble... | strange datasets from OSCAR corpus | 
From the [official site ](https://oscar-corpus.com/), the Yue Chinese dataset should have 2.2K... | 93 | strange datasets from OSCAR corpus
From the [official site ](https://oscar-corpus.com/), the... | [
0.26769956946372986,
0.05709494650363922,
-0.0023011192679405212,
0.5770171880722046,
0.15650038421154022,
0.06097047030925751,
-0.012423692271113396,
0.24926769733428955,
-0.26459363102912903,
0.048613112419843674,
-0.5796424746513367,
-0.1079980880022049,
0.04310471937060356,
-0.05117529... |
https://github.com/huggingface/datasets/issues/2391 | That could be useful indeed! Feel free to open a PR on the dataset card if you already have some code that runs, otherwise we'll take care of it soon :) | Missing original answers in kilt-TriviaQA | I previously opened an issue at https://github.com/facebookresearch/KILT/issues/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets
## Describe the bug
The `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative ... | 31 | Missing original answers in kilt-TriviaQA
I previously opened an issue at https://github.com/facebookresearch/KILT/issues/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets
## Describe the bug
The `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output'... | [
0.5064166188240051,
-0.34836792945861816,
-0.035132743418216705,
0.06285135447978973,
-0.12134288251399994,
-0.10113475471735,
0.034441981464624405,
0.31334057450294495,
0.12681017816066742,
0.18385657668113708,
0.1640152782201767,
0.49276721477508545,
0.1122974380850792,
0.405269145965576... |
https://github.com/huggingface/datasets/issues/2391 | I can open a PR but there is 2 details to fix:
- the name for the corresponding key (e.g. `original_answer`)
- how to implement it: I’m not sure what happens when you map `lambda x: {'input': ...}` as it keeps the other keys (e.g. `output`) intact but here since we want to set a nested value (e.g. `x['output']['origi... | Missing original answers in kilt-TriviaQA | I previously opened an issue at https://github.com/facebookresearch/KILT/issues/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets
## Describe the bug
The `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output']['answer']` contains a list of alternative ... | 84 | Missing original answers in kilt-TriviaQA
I previously opened an issue at https://github.com/facebookresearch/KILT/issues/42 but from the answer of @fabiopetroni it seems that the problem comes from HF-datasets
## Describe the bug
The `answer` field in kilt-TriviaQA, e.g. `kilt_tasks['train_triviaqa'][0]['output'... | [
0.5155784487724304,
-0.3537394404411316,
0.03276119381189346,
0.03958863019943237,
-0.1053859293460846,
-0.07997727394104004,
-0.029590442776679993,
0.2565179765224457,
0.08557366579771042,
0.13210302591323853,
0.0442916676402092,
0.5437099933624268,
0.19369834661483765,
0.4238482713699341... |
https://github.com/huggingface/datasets/issues/2387 | Looks like there are multiple issues regarding this (#2386, #2322) and it's a WIP #2329. Currently these datasets are being loaded in-memory which is causing this issue. Quoting @mariosasko here for a quick fix:
> set `keep_in_memory` to `False` when loading a dataset (`sst = load_dataset("sst", keep_in_memory=False... | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 69 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2387 | Hi ! Since `datasets` 1.6.0 we no longer keep small datasets (<250MB) on disk and load them in RAM instead by default. This makes data processing and iterating on data faster. However datasets in RAM currently have no way to reload previous results from the cache (since nothing is written on disk). We are working on ma... | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 106 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2387 | OK, It doesn't look like we can use the proposed workaround - see https://github.com/huggingface/transformers/issues/11801
Could you please add an env var for us to be able to turn off this unwanted in our situation behavior? It is really problematic for dev work, when one needs to restart the training very often an... | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 104 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2387 | Hi @stas00,
You are right: an env variable is needed to turn off this behavior. I am adding it.
For the moment there is a config parameter to turn off this behavior: `datasets.config.MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES = None`
You can find this info in the docs:
- in the docstring of the parameter `keep_in_m... | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 115 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2387 | Yes, but this still requires one to edit the standard example scripts, so if I'm doing that already I just as well can add `keep_in_memory=False`.
May be the low hanging fruit is to add `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES` env var to match the config, and if the user sets it to 0, then it'll be the same as `keep_in... | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 58 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2387 | @stas00, however, for the moment, setting the value to `0` is equivalent to the opposite, i.e. `keep_in_memory=True`. This means the max size until which I load in memory is 0 bytes.
Tell me if this is logical/convenient, or I should change it. | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 42 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2387 | In my PR, to turn off current default bahavior, you should set env variable to one of: `{"", "OFF", "NO", "FALSE"}`.
For example:
```
MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=
``` | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 26 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2387 | IMHO, this behaviour is not very intuitive, as 0 is a normal quantity of bytes. So `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=0` to me reads as don't cache ever.
Also "SIZE_IN_BYTES" that can take one of `{"", "OFF", "NO", "FALSE"}` is also quite odd.
I think supporting a very simple `MAX_IN_MEMORY_DATASET_SIZE_IN_BYTE... | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 89 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2387 | I understand your point @stas00, as I am not very convinced with current implementation.
My concern is: which numerical value should then pass a user who wants `keep_in_memory=True` by default, independently of dataset size? Currently it is `0` for this case. | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 41 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2387 | That's a good question, and again the normal bytes can be used for that:
```
MAX_IN_MEMORY_DATASET_SIZE_IN_BYTES=1e12 # (~2**40)
```
Since it's unlikely that anybody will have more than 1TB RAM.
It's also silly that it uses BYTES and not MBYTES - that level of refinement doesn't seem to be of a practical use in ... | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 127 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2387 | Great! Thanks, @stas00.
I am implementing your suggestion to turn off default value when set to `0`.
For the other suggestion (allowing different metric prefixes), I will discuss with @lhoestq to agree on its implementation. | datasets 1.6 ignores cache | Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/openwebtext10k/plain_text/1.0.0/... | 35 | datasets 1.6 ignores cache
Moving from https://github.com/huggingface/transformers/issues/11801#issuecomment-845546612
Quoting @VictorSanh:
>
> I downgraded datasets to `1.5.0` and printed `tokenized_datasets.cache_files` (L335):
>
> > `{'train': [{'filename': '/home/victor/.cache/huggingface/datasets/ope... | [
-0.21584859490394592,
0.02031104266643524,
0.042242735624313354,
0.140415221452713,
0.18217478692531586,
0.04623124748468399,
0.14098066091537476,
0.313957154750824,
0.05515870451927185,
-0.06445318460464478,
-0.23593822121620178,
-0.05794742703437805,
-0.02470097318291664,
-0.331577628850... |
https://github.com/huggingface/datasets/issues/2377 | Hi ! This is because we are actually using the arrow streaming format. We plan to switch to the arrow IPC format.
More info at #1933 | ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from pyarrow import feather
dataset = load_dataset('imdb', split='train')
dataset.save_to_disk('dataset_dir')
table = feather.read_table('dataset_dir/dataset.arro... | 26 | ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from pyarrow import feather
dataset = load_dataset('imdb', split='train')
data... | [
-0.2040329873561859,
0.1602187603712082,
-0.02409178391098976,
0.3715011179447174,
0.14897415041923523,
0.25441041588783264,
-0.0069092921912670135,
0.5975213050842285,
-0.5161232352256775,
-0.11885377764701843,
-0.3667036294937134,
0.6461246609687805,
-0.07226511836051941,
-0.747883617877... |
https://github.com/huggingface/datasets/issues/2377 | Not sure if this was resolved, but I am getting a similar error when trying to load a dataset.arrow file directly: `ArrowInvalid: Not an Arrow file` | ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from pyarrow import feather
dataset = load_dataset('imdb', split='train')
dataset.save_to_disk('dataset_dir')
table = feather.read_table('dataset_dir/dataset.arro... | 26 | ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from pyarrow import feather
dataset = load_dataset('imdb', split='train')
data... | [
-0.1831994652748108,
0.20106732845306396,
-0.007374458014965057,
0.4454070031642914,
0.1787397563457489,
0.25841856002807617,
-0.02225891500711441,
0.6304506063461304,
-0.514776885509491,
-0.17299571633338928,
-0.31670087575912476,
0.6856515407562256,
-0.07944105565547943,
-0.7523331642150... |
https://github.com/huggingface/datasets/issues/2377 | Since we're using the streaming format, you need to use `open_stream`:
```python
import pyarrow as pa
def in_memory_arrow_table_from_file(filename: str) -> pa.Table:
in_memory_stream = pa.input_stream(filename)
opened_stream = pa.ipc.open_stream(in_memory_stream)
pa_table = opened_stream.read_all(... | ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from pyarrow import feather
dataset = load_dataset('imdb', split='train')
dataset.save_to_disk('dataset_dir')
table = feather.read_table('dataset_dir/dataset.arro... | 49 | ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from pyarrow import feather
dataset = load_dataset('imdb', split='train')
data... | [
-0.21624460816383362,
0.11896523833274841,
-0.03757387399673462,
0.37340497970581055,
0.20306667685508728,
0.232123002409935,
-0.045416153967380524,
0.5734182000160217,
-0.551064133644104,
-0.09114573895931244,
-0.33249717950820923,
0.6647445559501648,
-0.06050475686788559,
-0.786835074424... |
https://github.com/huggingface/datasets/issues/2377 | > 由于我们使用流格式，因此您需要使用`open_stream`：
>
> ```python
> import pyarrow as pa
>
> def in_memory_arrow_table_from_file(filename: str) -> pa.Table:
> in_memory_stream = pa.input_stream(filename)
> opened_stream = pa.ipc.open_stream(in_memory_stream)
> pa_table = opened_stream.read_all()
> return pa_ta... | ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather | ## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from pyarrow import feather
dataset = load_dataset('imdb', split='train')
dataset.save_to_disk('dataset_dir')
table = feather.read_table('dataset_dir/dataset.arro... | 90 | ArrowDataset.save_to_disk produces files that cannot be read using pyarrow.feather
## Describe the bug
A clear and concise description of what the bug is.
## Steps to reproduce the bug
```python
from datasets import load_dataset
from pyarrow import feather
dataset = load_dataset('imdb', split='train')
data... | [
-0.18661513924598694,
0.15257158875465393,
-0.04614238440990448,
0.4061563014984131,
0.19235840439796448,
0.23081041872501373,
-0.04866486042737961,
0.5946786999702454,
-0.5452987551689148,
-0.10046915709972382,
-0.34359583258628845,
0.6925768256187439,
-0.06452371925115585,
-0.77320605516... |
https://github.com/huggingface/datasets/issues/2373 | Version below works, checked again in the docs, and data_files should be a path.
```
ds = datasets.load_dataset('my_script.py',
data_files='/data/dir/corpus.txt',
cache_dir='.')
``` | Loading dataset from local path | I'm trying to load a local dataset with the code below
```
ds = datasets.load_dataset('my_script.py',
data_files='corpus.txt',
data_dir='/data/dir',
cache_dir='.')
```
But internally a BuilderConfig is created, which tries to u... | 21 | Loading dataset from local path
I'm trying to load a local dataset with the code below
```
ds = datasets.load_dataset('my_script.py',
data_files='corpus.txt',
data_dir='/data/dir',
cache_dir='.')
```
But internally a BuilderC... | [
-0.36348921060562134,
0.2240849733352661,
0.05344425141811371,
0.45149776339530945,
0.1415300816297531,
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0.4987972676753998,
0.0649358481168747,
0.1139451265335083,
0.12790349125862122,
0.16706210374832153,
0.11647908389568329,
-0.000026594847440719604,
0.1618972122669... |
https://github.com/huggingface/datasets/issues/2350 | Just in case, this is a workaround that I use in my code and it seems to do the job.
```python
if use_gpu_index:
data["train"]._indexes["text_emb"].faiss_index = faiss.index_gpu_to_cpu(data["train"]._indexes["text_emb"].faiss_index)
``` | `FaissIndex.save` throws error on GPU | ## Describe the bug
After training an index with a factory string `OPQ16_128,IVF512,PQ32` on GPU, `.save_faiss_index` throws this error.
```
File "index_wikipedia.py", line 119, in <module>
data["train"].save_faiss_index("text_emb", index_save_path)
File "/home/vlialin/miniconda3/envs/cat/lib/python3.8... | 27 | `FaissIndex.save` throws error on GPU
## Describe the bug
After training an index with a factory string `OPQ16_128,IVF512,PQ32` on GPU, `.save_faiss_index` throws this error.
```
File "index_wikipedia.py", line 119, in <module>
data["train"].save_faiss_index("text_emb", index_save_path)
File "/home/v... | [
-0.11386904865503311,
0.23111632466316223,
0.049321435391902924,
0.21594278514385223,
0.3592372238636017,
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0.5158489942550659,
0.5090635418891907,
0.25088948011398315,
0.22490373253822327,
0.06693010777235031,
0.23696011304855347,
0.05093265697360039,
-0.068003647029399... |
https://github.com/huggingface/datasets/issues/2347 | Hi ! With @bhavitvyamalik we discussed about having something like
```python
from datasets import load_dataset_card
dataset_card = load_dataset_card("squad")
print(dataset_card.metadata.pretty_name)
# Stanford Question Answering Dataset (SQuAD)
print(dataset_card.metadata.languages)
# ["en"]
```
What do yo... | Add an API to access the language and pretty name of a dataset | It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts. | 95 | Add an API to access the language and pretty name of a dataset
It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Trans... | [
-0.18838688731193542,
0.11107870936393738,
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0.27057400345802307,
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0.2370298057794571,
0.1823674738407135,
0.530703604221344,
-0.220014289021492,
0.34264418482780457,
... |
https://github.com/huggingface/datasets/issues/2347 | What dataset_info method are you talking about @julien-c ? In `huggingface_hub` I can only see `model_info`. | Add an API to access the language and pretty name of a dataset | It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts. | 16 | Add an API to access the language and pretty name of a dataset
It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Trans... | [
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0.37463700771331787,
0.07790322601795197,
0.5090550780296326,
-0.060151200741529465,
0.384181737899... |
https://github.com/huggingface/datasets/issues/2347 | Indeed, this info can now be fetched with `huggingface_hub.dataset_info`, so I think we can close this issue. | Add an API to access the language and pretty name of a dataset | It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Transformers examples scripts. | 17 | Add an API to access the language and pretty name of a dataset
It would be super nice to have an API to get some metadata of the dataset from the name and args passed to `load_dataset`. This way we could programmatically infer the language and the name of a dataset when creating model cards automatically in the Trans... | [
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0.300954669713974,
0.04058237373828888,
0.4458237290382385,
-0.09999730437994003,
0.4816896617412567,
0... |
https://github.com/huggingface/datasets/issues/2345 | <s>Hi :) Can you share with us the code you used ?</s>
EDIT: from https://github.com/huggingface/transformers/issues/11665#issuecomment-838348291 I understand you're using the run_clm.py script. Can you share your logs ?
| [Question] How to move and reuse preprocessed dataset? | Hi, I am training a gpt-2 from scratch using run_clm.py.
I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),
I tried to :
copy path_to_cache_dir/datasets to new_cache_dir/datasets
set export HF_DATASETS_CACHE="new_cache_dir/"
but the program still re-preprocess the whole dataset... | 28 | [Question] How to move and reuse preprocessed dataset?
Hi, I am training a gpt-2 from scratch using run_clm.py.
I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),
I tried to :
copy path_to_cache_dir/datasets to new_cache_dir/datasets
set export HF_DATASETS_CACHE="new_cache_di... | [
-0.1601243019104004,
-0.3261336088180542,
0.0653829351067543,
0.3396511971950531,
0.2864598035812378,
0.17157326638698578,
0.13545748591423035,
0.18122419714927673,
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-0.15583719313144684,
-0.13779014348983765,
0.03987708315253258,
-0.1689593493938446,
0.058719940483570... |
https://github.com/huggingface/datasets/issues/2345 | Also note that for the caching to work, you must reuse the exact same parameters as in the first run. Did you change any parameter ? The `preprocessing_num_workers` should also stay the same | [Question] How to move and reuse preprocessed dataset? | Hi, I am training a gpt-2 from scratch using run_clm.py.
I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),
I tried to :
copy path_to_cache_dir/datasets to new_cache_dir/datasets
set export HF_DATASETS_CACHE="new_cache_dir/"
but the program still re-preprocess the whole dataset... | 33 | [Question] How to move and reuse preprocessed dataset?
Hi, I am training a gpt-2 from scratch using run_clm.py.
I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),
I tried to :
copy path_to_cache_dir/datasets to new_cache_dir/datasets
set export HF_DATASETS_CACHE="new_cache_di... | [
-0.23785926401615143,
-0.15300774574279785,
0.04165596514940262,
0.30848461389541626,
0.18331702053546906,
0.21348845958709717,
-0.057608652859926224,
0.25007739663124084,
-0.10958166420459747,
-0.22529210150241852,
-0.046464044600725174,
0.1580042541027069,
-0.12602244317531586,
-0.035717... |
https://github.com/huggingface/datasets/issues/2345 | > Also note that for the caching to work, you must reuse the exact same parameters as in the first run. Did you change any parameter ? The `preprocessing_num_workers` should also stay the same
I only changed the `preprocessing_num_workers` maybe it is the problem~ I will try again~ | [Question] How to move and reuse preprocessed dataset? | Hi, I am training a gpt-2 from scratch using run_clm.py.
I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),
I tried to :
copy path_to_cache_dir/datasets to new_cache_dir/datasets
set export HF_DATASETS_CACHE="new_cache_dir/"
but the program still re-preprocess the whole dataset... | 48 | [Question] How to move and reuse preprocessed dataset?
Hi, I am training a gpt-2 from scratch using run_clm.py.
I want to move and reuse the preprocessed dataset (It take 2 hour to preprocess),
I tried to :
copy path_to_cache_dir/datasets to new_cache_dir/datasets
set export HF_DATASETS_CACHE="new_cache_di... | [
-0.23027920722961426,
-0.18237048387527466,
0.04121704027056694,
0.340465784072876,
0.19807109236717224,
0.20862610638141632,
-0.037507135421037674,
0.2514612376689911,
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-0.0314362458884716,
0.16302579641342163,
-0.13057348132133484,
-0.04403072968... |
https://github.com/huggingface/datasets/issues/2344 | Hi ! We don't have `join`/`merge` on a certain column as in pandas.
Maybe you can just use the [concatenate_datasets](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=concatenate#datasets.concatenate_datasets) function.
| Is there a way to join multiple datasets in one? | **Is your feature request related to a problem? Please describe.**
I need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2?
**Describe the solution you'd like**
Id like to join them with a merge or join method, just like pandas dataframes.
**Add... | 21 | Is there a way to join multiple datasets in one?
**Is your feature request related to a problem? Please describe.**
I need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2?
**Describe the solution you'd like**
Id like to join them with a merge o... | [
-0.47733619809150696,
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0.17958742380142212,
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0.031471312046051025,
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0.20583799481391907,
0.480264276... |
https://github.com/huggingface/datasets/issues/2344 | Hi! You can use `datasets_sql` for that now. As of recently, PyArrow also supports querying tables via Substrait, so I think we can start adding these methods to the API soon. | Is there a way to join multiple datasets in one? | **Is your feature request related to a problem? Please describe.**
I need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2?
**Describe the solution you'd like**
Id like to join them with a merge or join method, just like pandas dataframes.
**Add... | 31 | Is there a way to join multiple datasets in one?
**Is your feature request related to a problem? Please describe.**
I need to join 2 datasets, one that is in the hub and another I've created from my files. Is there an easy way to join these 2?
**Describe the solution you'd like**
Id like to join them with a merge o... | [
-0.5119080543518066,
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0.12480822205543518,
0.15799444913864136,
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0.2924726903438568,
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0.14300329983234406,
0.2771616578102112,
0.13456672430038... |
https://github.com/huggingface/datasets/issues/2343 | Hi! Columns are removed **after** applying the function and **before** updating the examples with the function's output (as per the docs [here](https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.map.remove_columns)). I agree the docs on this should be more clear. | Columns are removed before or after map function applied? | ## Describe the bug
According to the documentation when applying map function the [remove_columns ](https://huggingface.co/docs/datasets/processing.html#removing-columns) will be removed after they are passed to the function, but in the [source code](https://huggingface.co/docs/datasets/package_reference/main_classes.... | 32 | Columns are removed before or after map function applied?
## Describe the bug
According to the documentation when applying map function the [remove_columns ](https://huggingface.co/docs/datasets/processing.html#removing-columns) will be removed after they are passed to the function, but in the [source code](https://... | [
0.3089645504951477,
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0.3217887878417969,
0.11714273691177368,
0.20059409737586975... |
https://github.com/huggingface/datasets/issues/2337 | I've raised a PR for this. Should work with `dataset = load_dataset("web_of_science", "WOS11967", ignore_verifications=True)`once it gets merged into the main branch. Thanks for reporting this! | NonMatchingChecksumError for web_of_science dataset | NonMatchingChecksumError when trying to download the web_of_science dataset.
>NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zip?dl=1']
Setting `ignore_verfications=True` results... | 25 | NonMatchingChecksumError for web_of_science dataset
NonMatchingChecksumError when trying to download the web_of_science dataset.
>NonMatchingChecksumError: Checksums didn't match for dataset source files:
['https://data.mendeley.com/datasets/9rw3vkcfy4/6/files/c9ea673d-5542-44c0-ab7b-f1311f7d61df/WebOfScience.zi... | [
-0.08515866100788116,
-0.07917472720146179,
-0.0687735378742218,
0.21224848926067352,
0.14462967216968536,
0.16705021262168884,
-0.06744646281003952,
0.3561592698097229,
0.3357114791870117,
0.17037680745124817,
-0.18412891030311584,
-0.08829440176486969,
0.03202468156814575,
-0.04910079017... |
https://github.com/huggingface/datasets/issues/2330 | I think the user could pass the `desc` parameter to `map` so that it can be displayed in the tqdm progress bar, as suggested by @cccntu.
When there's no multiprocessing, the `desc` of the progress bar could be the `desc` passed by the user.
In multiprocessing, we were already using a `desc` equal to `"#" + str(rank... | Allow passing `desc` to `tqdm` in `Dataset.map()` | It's normal to have many `map()` calls, and some of them can take a few minutes,
it would be nice to have a description on the progress bar.
Alternative solution:
Print the description before/after the `map()` call. | 145 | Allow passing `desc` to `tqdm` in `Dataset.map()`
It's normal to have many `map()` calls, and some of them can take a few minutes,
it would be nice to have a description on the progress bar.
Alternative solution:
Print the description before/after the `map()` call.
I think the user could pass the `desc` parame... | [
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0.03591986000537872,
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0.37445032596588135,
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0.2543700635433197,
0.2177436798810959,
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0.41625380516052246,
0.26747262477874756,
0.6729350090026855,
-0.0182512067258358,
0.0839984267950058,... |
https://github.com/huggingface/datasets/issues/2327 | cc @beurkinger but I think this has been fixed internally and will soon be updated right ? | A syntax error in example | 
Sorry to report with an image, I can't find the template source code of this snippet. | 17 | A syntax error in example
Sorry to report with an image, I can't find the template source code of this snippet.
cc @beurkinger but I think this has been fixed internally and will soon be updated right ? | [
0.07156356424093246,
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0.3997047245502472,
0.052938807755708694,
0.00390855967998... |
https://github.com/huggingface/datasets/issues/2323 | Thanks @ekeleshian for having reported.
I am closing this issue once that you updated `datasets`. Feel free to reopen it if the problem persists. | load_dataset("timit_asr") gives back duplicates of just one sample text | ## Describe the bug
When you look up on key ["train"] and then ['text'], you get back a list with just one sentence duplicated 4620 times. Namely, the sentence "Would such an act of refusal be useful?". Similarly when you look up ['test'] and then ['text'], the list is one sentence repeated "The bungalow was pleasant... | 24 | load_dataset("timit_asr") gives back duplicates of just one sample text
## Describe the bug
When you look up on key ["train"] and then ['text'], you get back a list with just one sentence duplicated 4620 times. Namely, the sentence "Would such an act of refusal be useful?". Similarly when you look up ['test'] and t... | [
0.2821882367134094,
-0.48903003334999084,
0.06362923979759216,
0.3835484981536865,
0.1550769954919815,
0.009322389960289001,
0.20255103707313538,
0.1971774697303772,
-0.19604328274726868,
0.022563952952623367,
-0.05378612130880356,
0.25881603360176086,
-0.06076086685061455,
-0.001916214823... |
https://github.com/huggingface/datasets/issues/2322 | I tried upgrading to `datasets==1.6.2` and downgrading to `1.6.0`. Both versions produce the same output.
Downgrading to `1.5.0` works and produces the following output for me:
```bash
Downloading: 9.20kB [00:00, 3.94MB/s]
Downloading: 5.99kB [00:00, 3.29MB/s]
No config sp... | Calls to map are not cached. | ## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):
print("executed", i[:10])... | 387 | Calls to map are not cached.
## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):... | [
-0.16529861092567444,
-0.39597147703170776,
-0.008847683668136597,
0.14566074311733246,
0.2062431275844574,
-0.10191763937473297,
0.3002680242061615,
0.12512916326522827,
0.3953084349632263,
0.020620327442884445,
-0.03697805106639862,
0.2428552359342575,
0.1699713170528412,
-0.284935235977... |
https://github.com/huggingface/datasets/issues/2322 | Hi,
set `keep_in_memory` to False when loading a dataset (`sst = load_dataset("sst", keep_in_memory=False)`) to prevent it from loading in-memory. Currently, in-memory datasets fail to find cached files due to this check (always False for them):
https://github.com/huggingface/datasets/blob/241a0b4a3a868778ee91e76... | Calls to map are not cached. | ## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):
print("executed", i[:10])... | 46 | Calls to map are not cached.
## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):... | [
-0.16529861092567444,
-0.39597147703170776,
-0.008847683668136597,
0.14566074311733246,
0.2062431275844574,
-0.10191763937473297,
0.3002680242061615,
0.12512916326522827,
0.3953084349632263,
0.020620327442884445,
-0.03697805106639862,
0.2428552359342575,
0.1699713170528412,
-0.284935235977... |
https://github.com/huggingface/datasets/issues/2322 | Hi @villmow, thanks for reporting.
As @mariosasko has pointed out, we did not consider this case when introducing the feature of automatic in-memory for small datasets. This needs to be fixed. | Calls to map are not cached. | ## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):
print("executed", i[:10])... | 31 | Calls to map are not cached.
## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):... | [
-0.16529861092567444,
-0.39597147703170776,
-0.008847683668136597,
0.14566074311733246,
0.2062431275844574,
-0.10191763937473297,
0.3002680242061615,
0.12512916326522827,
0.3953084349632263,
0.020620327442884445,
-0.03697805106639862,
0.2428552359342575,
0.1699713170528412,
-0.284935235977... |
https://github.com/huggingface/datasets/issues/2322 | Hi ! Currently a dataset that is in memory doesn't know doesn't know in which directory it has to read/write cache files.
On the other hand, a dataset that loaded from the disk (via memory mapping) uses the directory from which the dataset is located to read/write cache files.
Because of that, currently in-memory d... | Calls to map are not cached. | ## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):
print("executed", i[:10])... | 82 | Calls to map are not cached.
## Describe the bug
Somehow caching does not work for me anymore. Am I doing something wrong, or is there anything that I missed?
## Steps to reproduce the bug
```python
import datasets
datasets.set_caching_enabled(True)
sst = datasets.load_dataset("sst")
def foo(samples, i):... | [
-0.16529861092567444,
-0.39597147703170776,
-0.008847683668136597,
0.14566074311733246,
0.2062431275844574,
-0.10191763937473297,
0.3002680242061615,
0.12512916326522827,
0.3953084349632263,
0.020620327442884445,
-0.03697805106639862,
0.2428552359342575,
0.1699713170528412,
-0.284935235977... |
https://github.com/huggingface/datasets/issues/2319 | Thanks for reporting, @sgraaf.
I am going to have a look at it.
I guess the expected codec is "UTF-8". Normally, when no explicitly codec is passed, Python uses one which is platform-dependent. For Linux machines, the default codec is `utf_8`, which is OK. However for Windows machine, the default codec is `cp125... | UnicodeDecodeError for OSCAR (Afrikaans) | ## Describe the bug
When loading the [OSCAR dataset](https://huggingface.co/datasets/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_af")
```... | 57 | UnicodeDecodeError for OSCAR (Afrikaans)
## Describe the bug
When loading the [OSCAR dataset](https://huggingface.co/datasets/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset(... | [
-0.06944210827350616,
-0.06897582113742828,
-0.06865903735160828,
0.546219527721405,
0.49986982345581055,
0.02358095347881317,
0.16044245660305023,
0.16006386280059814,
-0.25320762395858765,
0.26161664724349976,
0.10814107954502106,
0.07456546276807785,
-0.13298732042312622,
-0.16212666034... |
https://github.com/huggingface/datasets/issues/2319 | @sgraaf, I have just merged the fix in the master branch.
You can either:
- install `datasets` from source code
- wait until we make the next release of `datasets`
- set the `utf-8` codec as your default instead of `cp1252`. This can be done by activating the Python [UTF-8 mode](https://www.python.org/dev/peps/pe... | UnicodeDecodeError for OSCAR (Afrikaans) | ## Describe the bug
When loading the [OSCAR dataset](https://huggingface.co/datasets/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset("oscar", "unshuffled_deduplicated_af")
```... | 66 | UnicodeDecodeError for OSCAR (Afrikaans)
## Describe the bug
When loading the [OSCAR dataset](https://huggingface.co/datasets/oscar) (specifically `unshuffled_deduplicated_af`), I encounter a `UnicodeDecodeError`.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dataset = load_dataset(... | [
-0.06944210827350616,
-0.06897582113742828,
-0.06865903735160828,
0.546219527721405,
0.49986982345581055,
0.02358095347881317,
0.16044245660305023,
0.16006386280059814,
-0.25320762395858765,
0.26161664724349976,
0.10814107954502106,
0.07456546276807785,
-0.13298732042312622,
-0.16212666034... |
https://github.com/huggingface/datasets/issues/2318 | Hi @richardliaw,
First, thanks for the compliments.
In relation with your request, currently, the dynamic modules path is obtained this way:
```python
from datasets.load import init_dynamic_modules, MODULE_NAME_FOR_DYNAMIC_MODULES
dynamic_modules_path = init_dynamic_modules(MODULE_NAME_FOR_DYNAMIC_MODULES)
... | [api request] API to obtain "dataset_module" dynamic path? | **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
This is an awesome library.
It seems like the dynamic module path in this library has broken some of hyperparameter tuning functionality: https://discuss.huggingface.co/t/using-hyperparamet... | 63 | [api request] API to obtain "dataset_module" dynamic path?
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
This is an awesome library.
It seems like the dynamic module path in this library has broken some of hyperparameter tuning funct... | [
-0.10380320996046066,
-0.2795986533164978,
-0.13102875649929047,
0.052743010222911835,
0.32544711232185364,
-0.26348745822906494,
-0.06139513850212097,
0.15633627772331238,
-0.21275481581687927,
0.33938926458358765,
-0.07494547963142395,
0.7778294086456299,
-0.47252270579338074,
0.31665450... |
https://github.com/huggingface/datasets/issues/2318 | Hi @richardliaw, the feature is on the master branch and will be included in the next release in a couple of weeks. | [api request] API to obtain "dataset_module" dynamic path? | **Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
This is an awesome library.
It seems like the dynamic module path in this library has broken some of hyperparameter tuning functionality: https://discuss.huggingface.co/t/using-hyperparamet... | 22 | [api request] API to obtain "dataset_module" dynamic path?
**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is.
This is an awesome library.
It seems like the dynamic module path in this library has broken some of hyperparameter tuning funct... | [
-0.1270400583744049,
-0.22530506551265717,
-0.13680431246757507,
0.12058562785387039,
0.2940102517604828,
-0.30054840445518494,
-0.08337728679180145,
0.1747506707906723,
-0.21367301046848297,
0.3675941526889801,
-0.0689018964767456,
0.7768816351890564,
-0.46218401193618774,
0.3776901364326... |
https://github.com/huggingface/datasets/issues/2301 | Hi @gchhablani,
There are some 3rd-party dependencies that require to build code in C. In this case, it is the library `python-Levenshtein`.
On Windows, in order to be able to build C code, you need to install at least `Microsoft C++ Build Tools` version 14. You can find more info here: https://visualstudio.micr... | Unable to setup dev env on Windows | Hi
I tried installing the `".[dev]"` version on Windows 10 after cloning.
Here is the error I'm facing:
```bat
(env) C:\testing\datasets>pip install -e ".[dev]"
Obtaining file:///C:/testing/datasets
Requirement already satisfied: numpy>=1.17 in c:\programdata\anaconda3\envs\env\lib\site-packages (from datas... | 52 | Unable to setup dev env on Windows
Hi
I tried installing the `".[dev]"` version on Windows 10 after cloning.
Here is the error I'm facing:
```bat
(env) C:\testing\datasets>pip install -e ".[dev]"
Obtaining file:///C:/testing/datasets
Requirement already satisfied: numpy>=1.17 in c:\programdata\anaconda3\e... | [
-0.41213446855545044,
-0.08557815104722977,
-0.09772773087024689,
-0.13878114521503448,
0.3114473521709442,
0.06837527453899384,
0.344851553440094,
0.046587489545345306,
-0.10013041645288467,
0.15623459219932556,
0.04337955266237259,
0.24242639541625977,
0.15818417072296143,
0.216297000646... |
https://github.com/huggingface/datasets/issues/2300 | I'm happy to take this on:) One question: The original unlabelled data is stored unsegmented (see e.g. https://github.com/facebookresearch/voxpopuli/blob/main/voxpopuli/get_unlabelled_data.py#L30), but segmenting the audio in the dataset would require a dependency on something like soundfile or torchaudio. An alternati... | Add VoxPopuli | ## Adding a Dataset
- **Name:** Voxpopuli
- **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings
- **Paper:** https://arxiv.org/abs/2101.00390
- **Data:** https://github.com/facebookresearch/voxpopuli
- **Motivation:** biggest unlabeled speech dataset
**Note**:... | 65 | Add VoxPopuli
## Adding a Dataset
- **Name:** Voxpopuli
- **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings
- **Paper:** https://arxiv.org/abs/2101.00390
- **Data:** https://github.com/facebookresearch/voxpopuli
- **Motivation:** biggest unlabeled speech data... | [
-0.2925267517566681,
0.2328723818063736,
-0.04869353398680687,
-0.07535172998905182,
-0.15587887167930603,
-0.18521828949451447,
0.38092970848083496,
0.21007290482521057,
-0.026084307581186295,
0.25481802225112915,
-0.2955556809902191,
0.09394417703151703,
-0.5140134692192078,
0.2282819449... |
https://github.com/huggingface/datasets/issues/2300 | Hey @jfainberg,
This sounds great! I think adding a dependency would not be a big problem, however automatically segmenting the data probably means that it would take a very long time to do:
```python
dataset = load_dataset("voxpopuli", "french")
```
=> so as a start I think your option 2 is the way to go! | Add VoxPopuli | ## Adding a Dataset
- **Name:** Voxpopuli
- **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings
- **Paper:** https://arxiv.org/abs/2101.00390
- **Data:** https://github.com/facebookresearch/voxpopuli
- **Motivation:** biggest unlabeled speech dataset
**Note**:... | 54 | Add VoxPopuli
## Adding a Dataset
- **Name:** Voxpopuli
- **Description:** VoxPopuli is raw data is collected from 2009-2020 European Parliament event recordings
- **Paper:** https://arxiv.org/abs/2101.00390
- **Data:** https://github.com/facebookresearch/voxpopuli
- **Motivation:** biggest unlabeled speech data... | [
-0.36220473051071167,
0.2060508131980896,
-0.03822983428835869,
0.0014164969325065613,
-0.12140680849552155,
-0.1296064406633377,
0.2858927547931671,
0.34885531663894653,
0.18100407719612122,
0.2673177123069763,
-0.12204407155513763,
0.17343297600746155,
-0.5791189670562744,
0.248656809329... |
https://github.com/huggingface/datasets/issues/2294 | Hi ! Have you tried other values for `preprocessing_num_workers` ? Is it always process 0 that is slower ?
There are no difference between process 0 and the others except that it processes the first shard of the dataset. | Slow #0 when using map to tokenize. | Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
loa... | 39 | Slow #0 when using map to tokenize.
Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_... | [
-0.4360864758491516,
-0.33404165506362915,
-0.033181264996528625,
-0.055587250739336014,
0.06970161199569702,
-0.19447870552539825,
0.3812374472618103,
0.2073155790567398,
-0.274800181388855,
0.05177777633070946,
0.31575703620910645,
0.3878781795501709,
-0.23858904838562012,
-0.00817088037... |
https://github.com/huggingface/datasets/issues/2294 | Hi, I have found the reason of it. Before using the map function to tokenize the data, I concatenate the wikipedia and bookcorpus first, like this:
```if args.dataset_name1 is not None:
dataset1 = load_dataset(args.dataset_name1, args.dataset_config_name1, split="train")
dataset1 = dataset1.remove_co... | Slow #0 when using map to tokenize. | Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
loa... | 172 | Slow #0 when using map to tokenize.
Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_... | [
-0.4216778874397278,
-0.24637676775455475,
-0.00015586242079734802,
0.06709486246109009,
0.030975334346294403,
-0.20856627821922302,
0.43102556467056274,
0.22374974191188812,
-0.25451672077178955,
0.04507310688495636,
0.23347431421279907,
0.3255724608898163,
-0.14147138595581055,
0.0176056... |
https://github.com/huggingface/datasets/issues/2294 | That makes sense ! You can indeed use `map` on both datasets separately and then concatenate.
Another option is to concatenate, then shuffle, and then `map`. | Slow #0 when using map to tokenize. | Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_columns=column_names,
loa... | 26 | Slow #0 when using map to tokenize.
Hi, _datasets_ is really amazing! I am following [run_mlm_no_trainer.py](url) to pre-train BERT, and it uses `tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=args.preprocessing_num_workers,
remove_... | [
-0.4388078451156616,
-0.2752535939216614,
-0.0035831183195114136,
-0.019504789263010025,
0.037655048072338104,
-0.16884642839431763,
0.38462021946907043,
0.24709677696228027,
-0.26957133412361145,
0.07264755666255951,
0.1870824694633484,
0.34464672207832336,
-0.198602095246315,
0.034010127... |
https://github.com/huggingface/datasets/issues/2288 | Hi,
this is not a standard CSV file (requires additional preprocessing) so I wouldn't label this as s bug. You could parse the examples with the regex module or the string API to extract the data, but the following approach is probably the easiest (once you load the data):
```python
import ast
# load the dataset ... | Load_dataset for local CSV files | The method load_dataset fails to correctly load a dataset from csv.
Moreover, I am working on a token-classification task ( POS tagging) , where each row in my CSV contains two columns each of them having a list of strings.
row example:
```tokens | labels
['I' , 'am', 'John'] | ['PRON', 'AUX', 'PROPN' ]
``... | 72 | Load_dataset for local CSV files
The method load_dataset fails to correctly load a dataset from csv.
Moreover, I am working on a token-classification task ( POS tagging) , where each row in my CSV contains two columns each of them having a list of strings.
row example:
```tokens | labels
['I' , 'am', 'John']... | [
-0.11043224483728409,
-0.04609934985637665,
0.001793045550584793,
0.07474763691425323,
0.4480467438697815,
0.06277740001678467,
0.45521390438079834,
0.21968252956867218,
0.29948997497558594,
-0.13296687602996826,
0.20607569813728333,
0.4624341130256653,
-0.07284626364707947,
0.079717501997... |
https://github.com/huggingface/datasets/issues/2288 | Hi,
Thanks for the reply.
I have already used ```ast.literal_eval``` to evaluate the string into list, but I was getting another error:
```
ArrowInvalid: Could not convert X with type str: tried to convert to int
```
Why this happens ? Should labels be mapped to their ids and use int instead of str ? | Load_dataset for local CSV files | The method load_dataset fails to correctly load a dataset from csv.
Moreover, I am working on a token-classification task ( POS tagging) , where each row in my CSV contains two columns each of them having a list of strings.
row example:
```tokens | labels
['I' , 'am', 'John'] | ['PRON', 'AUX', 'PROPN' ]
``... | 55 | Load_dataset for local CSV files
The method load_dataset fails to correctly load a dataset from csv.
Moreover, I am working on a token-classification task ( POS tagging) , where each row in my CSV contains two columns each of them having a list of strings.
row example:
```tokens | labels
['I' , 'am', 'John']... | [
-0.17451190948486328,
-0.10917867720127106,
-0.023106930777430534,
0.09802357852458954,
0.4885462820529938,
0.02145753800868988,
0.4982636570930481,
0.22232067584991455,
0.3661424517631531,
-0.05790647119283676,
0.022860080003738403,
0.5268846154212952,
-0.04110350087285042,
0.178615689277... |
https://github.com/huggingface/datasets/issues/2285 |
I received an answer for this question on the HuggingFace Datasets forum by @lhoestq
Hi !
If you want to tokenize line by line, you can use this:
```
max_seq_length = 512
num_proc = 4
def tokenize_function(examples):
# Remove empty lines
examples["text"] = [line for line in examples["text"] if len(lin... | Help understanding how to build a dataset for language modeling as with the old TextDataset | Hello,
I am trying to load a custom dataset that I will then use for language modeling. The dataset consists of a text file that has a whole document in each line, meaning that each line overpasses the normal 512 tokens limit of most tokenizers.
I would like to understand what is the process to build a text datas... | 270 | Help understanding how to build a dataset for language modeling as with the old TextDataset
Hello,
I am trying to load a custom dataset that I will then use for language modeling. The dataset consists of a text file that has a whole document in each line, meaning that each line overpasses the normal 512 tokens lim... | [
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0.18944397568702698,
-0.19410620629787445,
-0.16465245187282562,
0.134024... |
https://github.com/huggingface/datasets/issues/2279 | From the trace this seems like an error in the tokenizer library instead.
Do you mind opening an issue at https://github.com/huggingface/tokenizers instead? | Compatibility with Ubuntu 18 and GLIBC 2.27? | ## Describe the bug
For use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https://www.digitalocean.com/community/questions/how-install-glibc-2-29-or-higher-in-ubuntu-18-04).
I'm not sure... | 22 | Compatibility with Ubuntu 18 and GLIBC 2.27?
## Describe the bug
For use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https://www.digitalocean.com/community/questions/how-install-glibc-2-... | [
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-0.2015923261642456,
0.020794443786... |
https://github.com/huggingface/datasets/issues/2279 | Hi @tginart, thanks for reporting.
I think this issue is already open at `tokenizers` library: https://github.com/huggingface/tokenizers/issues/685 | Compatibility with Ubuntu 18 and GLIBC 2.27? | ## Describe the bug
For use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https://www.digitalocean.com/community/questions/how-install-glibc-2-29-or-higher-in-ubuntu-18-04).
I'm not sure... | 16 | Compatibility with Ubuntu 18 and GLIBC 2.27?
## Describe the bug
For use on Ubuntu systems, it seems that datasets requires GLIBC 2.29. However, Ubuntu 18 runs with GLIBC 2.27 and it seems [non-trivial to upgrade GLIBC to 2.29 for Ubuntu 18 users](https://www.digitalocean.com/community/questions/how-install-glibc-2-... | [
-0.1428118795156479,
-0.17239271104335785,
0.10753285139799118,
0.13023461401462555,
0.05963866785168648,
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0.011138416826725006,
0.17404615879058838,
0.22679001092910767,
-0.2015923261642456,
0.020794443786... |
https://github.com/huggingface/datasets/issues/2278 | Hi ! I think you might have to ask on the `transformers` repo on or the forum at https://discuss.huggingface.co/
Closing since it's not related to this library | Loss result inGptNeoForCasual | Is there any way you give the " loss" and "logits" results in the gpt neo api? | 27 | Loss result inGptNeoForCasual
Is there any way you give the " loss" and "logits" results in the gpt neo api?
Hi ! I think you might have to ask on the `transformers` repo on or the forum at https://discuss.huggingface.co/
Closing since it's not related to this library | [
-0.1725194752216339,
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0.14229059219360352,
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0.10090497136116028,
0.254296541... |
https://github.com/huggingface/datasets/issues/2276 | Therefore, when I try to concatenate larger datasets (5x 35GB data sets) I also get an out of memory error, since over 90GB of swap space was used at the time of the crash:
```
---------------------------------------------------------------------------
MemoryError Traceback (most rece... | concatenate_datasets loads all the data into memory | ## Describe the bug
When I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
 , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
![image]... | [
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0.21090885996818542,
0.005909480154514313,
-0.2283867299... |
https://github.com/huggingface/datasets/issues/2276 | Hi ! this looks like an important issue. Let me try to reproduce this.
Cc @samsontmr this might be related to the memory issue you have in #2134 | concatenate_datasets loads all the data into memory | ## Describe the bug
When I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
 , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
![image]... | [
-0.16557928919792175,
0.008643925189971924,
0.025085650384426117,
0.38551655411720276,
0.2144165337085724,
0.13296078145503998,
0.00009696092456579208,
0.23910781741142273,
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0.04352058470249176,
0.10074502974748611,
0.1849096268415451,
0.13490596413612366,
-0.196389690... |
https://github.com/huggingface/datasets/issues/2276 | @lhoestq Just went to open a similar issue.
It seems like deep copying (tested on master) the dataset object writes the table's record batches (`dset._data._batches`) into RAM.
To find the bug, I modified the `_deepcopy` function in `table.py` as follows:
```python
def _deepcopy(x, memo: dict):
"""deepcopy... | concatenate_datasets loads all the data into memory | ## Describe the bug
When I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
 , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
![image]... | [
-0.11895914375782013,
0.08330535888671875,
0.1260729432106018,
0.37199756503105164,
0.017117120325565338,
0.11338713765144348,
-0.11379154771566391,
0.3422016501426697,
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-0.0313396081328392,
-0.0029889456927776337,
0.28330928087234497,
0.10329969972372055,
-0.1568291038... |
https://github.com/huggingface/datasets/issues/2276 | Thanks for the insights @mariosasko ! I'm working on a fix.
Since this is a big issue I'll make a patch release as soon as this is fixed | concatenate_datasets loads all the data into memory | ## Describe the bug
When I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
 , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
![image]... | [
-0.13985125720500946,
0.015114620327949524,
0.05767040699720383,
0.36967381834983826,
0.18395677208900452,
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0.26692160964012146,
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0.008929267525672913,
0.07352760434150696,
0.18218256533145905,
0.10922588407993317,
-0.204664915... |
https://github.com/huggingface/datasets/issues/2276 | Hi @samsontmr @TaskManager91 the fix is on the master branch, feel free to install `datasets` from source and let us know if you still have issues | concatenate_datasets loads all the data into memory | ## Describe the bug
When I try to concatenate 2 datasets (10GB each) , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
 , the entire data is loaded into memory instead of being written directly to disk.
Interestingly, this happens when trying to save the new dataset to disk or concatenating it again.
![image]... | [
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0.029283933341503143,
0.3677370846271515,
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0.2557007372379303,
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0.02055460959672928,
0.07563759386539459,
0.2006983757019043,
0.11371898651123047,
-0.19137379527091... |
https://github.com/huggingface/datasets/issues/2275 | Hi @puzzler10,
Those examples where `gold_label` field was empty, -1 label was alloted to it. In order to remove it you can filter the samples from train/val/test splits. Here's how you can drop those rows from the dataset:
`dataset = load_dataset("snli")`
`dataset_test_filter = dataset['test'].filter(lambda exampl... | SNLI dataset has labels of -1 | There are a number of rows with a label of -1 in the SNLI dataset. The dataset descriptions [here](https://nlp.stanford.edu/projects/snli/) and [here](https://github.com/huggingface/datasets/tree/master/datasets/snli) don't list -1 as a label possibility, and neither does the dataset viewer. As examples, see index 107... | 69 | SNLI dataset has labels of -1
There are a number of rows with a label of -1 in the SNLI dataset. The dataset descriptions [here](https://nlp.stanford.edu/projects/snli/) and [here](https://github.com/huggingface/datasets/tree/master/datasets/snli) don't list -1 as a label possibility, and neither does the dataset v... | [
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0.22228220105171204,
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0.24445928633213043,
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0.4890281856060028,
-0.11607945710420609,
0.2643213272094... |
https://github.com/huggingface/datasets/issues/2272 | This has been fixed in this commit: https://github.com/huggingface/datasets/pull/2254/commits/88676c930216cd4cc31741b99827b477d2b46cb6
It was introduced in #2246 : using map with `input_columns` doesn't return the other columns anymore | Bug in Dataset.class_encode_column | ## Describe the bug
All the rest of the columns except the one passed to `Dataset.class_encode_column` are discarded.
## Expected results
All the original columns should be kept.
This needs regression tests.
| 24 | Bug in Dataset.class_encode_column
## Describe the bug
All the rest of the columns except the one passed to `Dataset.class_encode_column` are discarded.
## Expected results
All the original columns should be kept.
This needs regression tests.
This has been fixed in this commit: https://github.com/hugg... | [
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... |
https://github.com/huggingface/datasets/issues/2267 | I'm not able to reproduce this, do you think you can provide a code that creates a DatasetDict that has this issue when saving and reloading ? | DatasetDict save load Failing test in 1.6 not in 1.5 | ## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a dataset dict from jsonl
path = '/test/foo'
ds_dict.s... | 27 | DatasetDict save load Failing test in 1.6 not in 1.5
## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a data... | [
-0.19468021392822266,
0.1140984296798706,
-0.012763917446136475,
0.2849428355693817,
0.06459204107522964,
-0.061417266726493835,
0.13234004378318787,
0.4565902352333069,
0.44223761558532715,
0.1792287528514862,
0.23589664697647095,
0.3808513581752777,
0.054587047547101974,
-0.0561683923006... |
https://github.com/huggingface/datasets/issues/2267 | Hi, I just ran into a similar error. Here is the minimal code to reproduce:
```python
from datasets import load_dataset, DatasetDict
ds = load_dataset('super_glue', 'multirc')
ds.save_to_disk('tempds')
ds = DatasetDict.load_from_disk('tempds')
```
```bash
Reusing dataset super_glue (/home/idahl/.cache/h... | DatasetDict save load Failing test in 1.6 not in 1.5 | ## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a dataset dict from jsonl
path = '/test/foo'
ds_dict.s... | 226 | DatasetDict save load Failing test in 1.6 not in 1.5
## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a data... | [
-0.19468021392822266,
0.1140984296798706,
-0.012763917446136475,
0.2849428355693817,
0.06459204107522964,
-0.061417266726493835,
0.13234004378318787,
0.4565902352333069,
0.44223761558532715,
0.1792287528514862,
0.23589664697647095,
0.3808513581752777,
0.054587047547101974,
-0.0561683923006... |
https://github.com/huggingface/datasets/issues/2267 | My current workaround is to remove the idx feature:
```
from datasets import load_dataset, DatasetDict, Value
ds = load_dataset('super_glue', 'multirc')
ds = ds.remove_columns('idx')
ds.save_to_disk('tempds')
ds = DatasetDict.load_from_disk('tempds')
```
works. | DatasetDict save load Failing test in 1.6 not in 1.5 | ## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a dataset dict from jsonl
path = '/test/foo'
ds_dict.s... | 29 | DatasetDict save load Failing test in 1.6 not in 1.5
## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a data... | [
-0.19468021392822266,
0.1140984296798706,
-0.012763917446136475,
0.2849428355693817,
0.06459204107522964,
-0.061417266726493835,
0.13234004378318787,
0.4565902352333069,
0.44223761558532715,
0.1792287528514862,
0.23589664697647095,
0.3808513581752777,
0.054587047547101974,
-0.0561683923006... |
https://github.com/huggingface/datasets/issues/2267 | It looks like this issue comes from the order of the fields in the 'idx' struct that is different for some reason.
I'm looking into it. Note that as a workaround you can also flatten the nested features with `ds = ds.flatten()` | DatasetDict save load Failing test in 1.6 not in 1.5 | ## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a dataset dict from jsonl
path = '/test/foo'
ds_dict.s... | 42 | DatasetDict save load Failing test in 1.6 not in 1.5
## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a data... | [
-0.19468021392822266,
0.1140984296798706,
-0.012763917446136475,
0.2849428355693817,
0.06459204107522964,
-0.061417266726493835,
0.13234004378318787,
0.4565902352333069,
0.44223761558532715,
0.1792287528514862,
0.23589664697647095,
0.3808513581752777,
0.054587047547101974,
-0.0561683923006... |
https://github.com/huggingface/datasets/issues/2267 | I just pushed a fix on `master`. We'll do a new release soon !
Thanks for reporting | DatasetDict save load Failing test in 1.6 not in 1.5 | ## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a dataset dict from jsonl
path = '/test/foo'
ds_dict.s... | 17 | DatasetDict save load Failing test in 1.6 not in 1.5
## Describe the bug
We have a test that saves a DatasetDict to disk and then loads it from disk. In 1.6 there is an incompatibility in the schema.
Downgrading to `>1.6` -- fixes the problem.
## Steps to reproduce the bug
```python
### Load a data... | [
-0.19468021392822266,
0.1140984296798706,
-0.012763917446136475,
0.2849428355693817,
0.06459204107522964,
-0.061417266726493835,
0.13234004378318787,
0.4565902352333069,
0.44223761558532715,
0.1792287528514862,
0.23589664697647095,
0.3808513581752777,
0.054587047547101974,
-0.0561683923006... |
https://github.com/huggingface/datasets/issues/2262 | Thanks @bhavitvyamalik for the fix !
The fix will be available in the next release.
It's already available on the `master` branch. For now you can either install `datasets` from source or use `script_version="master"` in `load_dataset` to use the fixed version of this dataset. | NewsPH NLI dataset script fails to access test data. | In Newsph-NLI Dataset (#1192), it fails to access test data.
According to the script below, the download manager will download the train data when trying to download the test data.
https://github.com/huggingface/datasets/blob/2a2dd6316af2cc7fdf24e4779312e8ee0c7ed98b/datasets/newsph_nli/newsph_nli.py#L71
If yo... | 44 | NewsPH NLI dataset script fails to access test data.
In Newsph-NLI Dataset (#1192), it fails to access test data.
According to the script below, the download manager will download the train data when trying to download the test data.
https://github.com/huggingface/datasets/blob/2a2dd6316af2cc7fdf24e4779312e8ee... | [
-0.16071613132953644,
0.28491076827049255,
-0.1269761025905609,
0.17550434172153473,
0.08590202033519745,
0.04028964787721634,
0.25620752573013306,
0.36588478088378906,
0.04193354398012161,
0.3072819113731384,
0.005247794091701508,
0.04337183013558388,
0.015890851616859436,
0.2191218435764... |
https://github.com/huggingface/datasets/issues/2256 | Thanks for reporting ! We are working on this and we'll do a patch release very soon. | Running `datase.map` with `num_proc > 1` uses a lot of memory | ## Describe the bug
Running `datase.map` with `num_proc > 1` leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dstc8_datset = load_dataset("roskoN/dstc8-reddit-corpus", keep_in_memory=False)
... | 17 | Running `datase.map` with `num_proc > 1` uses a lot of memory
## Describe the bug
Running `datase.map` with `num_proc > 1` leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dstc8_datset = load... | [
-0.0754542201757431,
-0.30183207988739014,
-0.04138416051864624,
0.35142216086387634,
0.20282042026519775,
0.09861710667610168,
0.09969024360179901,
0.2842756509780884,
0.27235549688339233,
0.22350537776947021,
0.1852625012397766,
0.43167802691459656,
-0.14579516649246216,
-0.0435100197792... |
https://github.com/huggingface/datasets/issues/2256 | We did a patch release to fix this issue.
It should be fixed in the new version 1.6.1
Thanks again for reporting and for the details :) | Running `datase.map` with `num_proc > 1` uses a lot of memory | ## Describe the bug
Running `datase.map` with `num_proc > 1` leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dstc8_datset = load_dataset("roskoN/dstc8-reddit-corpus", keep_in_memory=False)
... | 27 | Running `datase.map` with `num_proc > 1` uses a lot of memory
## Describe the bug
Running `datase.map` with `num_proc > 1` leads to a tremendous memory usage that requires swapping on disk and it becomes very slow.
## Steps to reproduce the bug
```python
from datasets import load_dataset
dstc8_datset = load... | [
-0.0729508325457573,
-0.29298192262649536,
-0.04242730140686035,
0.37070685625076294,
0.2069273144006729,
0.08793412148952484,
0.08913224935531616,
0.2784177362918854,
0.2625883221626282,
0.22960984706878662,
0.1797681748867035,
0.4309556782245636,
-0.1489771008491516,
-0.0277889221906662,... |
https://github.com/huggingface/datasets/issues/2252 | Hi ! Sorry to hear that. This may come from another issue then.
First can we check if this latency comes from the dataset itself ?
You can try to load your dataset and benchmark the speed of querying random examples inside it ?
```python
import time
import numpy as np
from datasets import load_from_disk
da... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 101 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Hi @lhoestq, here is the result. I additionally measured time to `load_from_disk`:
* 60GB
```
loading took: 22.618776321411133
ramdom indexing 100 times took: 0.10214924812316895
```
* 600GB
```
loading took: 1176.1764674186707
ramdom indexing 100 times took: 2.853600025177002
```
Hmm.. I double checke... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 59 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | I'm surprised by the speed change. Can you give more details about your dataset ?
The speed depends on the number of batches in the arrow tables and the distribution of the lengths of the batches.
You can access the batches by doing `dataset.data.to_batches()` (use only for debugging) (it doesn't bring data in memory... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 84 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Also if you could give us more info about your env like your OS, version of pyarrow and if you're using an HDD or a SSD | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 26 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Here are some details of my 600GB dataset. This is a dataset AFTER the `map` function and once I load this dataset, I do not use `map` anymore in the training. Regarding the distribution of the lengths, it is almost uniform (90% is 512 tokens, and 10% is randomly shorter than that -- typical setting for language modeli... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 118 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Regarding the environment, I am running the code on a cloud server. Here are some info:
```
Ubuntu 18.04.5 LTS # cat /etc/issue
pyarrow 3.0.0 # pip list | grep pyarrow
```
The data is stored in SSD and it is mounted to the machine via Network File System.
If you could point me to some of the ... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 76 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | I am not sure how I could provide you with the reproducible code, since the problem only arises when the data is big. For the moment, I would share the part that I think is relevant. Feel free to ask me for more info.
```python
class MyModel(pytorch_lightning.LightningModule)
def setup(self, stage):
s... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 71 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Hi ! Sorry for the delay I haven't had a chance to take a look at this yet. Are you still experiencing this issue ?
I'm asking because the latest patch release 1.6.2 fixed a few memory issues that could have lead to slow downs | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 45 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Hi! I just ran the same code with different datasets (one is 60 GB and another 600 GB), and the latter runs much slower. ETA differs by 10x. | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 28 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | @lhoestq and @hwijeen
Despite upgrading to datasets 1.6.2, still experiencing extremely slow (2h00) loading for a 300Gb local dataset shard size 1.1Gb on local HDD (40Mb/s read speed). This corresponds almost exactly to total data divided by reading speed implying that it reads the entire dataset at each load.
St... | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 227 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
https://github.com/huggingface/datasets/issues/2252 | Hi @lhoestq thanks for the quick turn-around, actually the plain vanilla way, without an particular knack or fashion, I tried to look into the documentation for some alternative but couldn't find any
> dataset = load_from_disk(dataset_path=os.path.join(datasets_dir,dataset_dir)) | Slow dataloading with big datasets issue persists | Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action | Mean duration (s) |Num calls | Total ... | 36 | Slow dataloading with big datasets issue persists
Hi,
I reported too slow data fetching when data is large(#2210) a couple of weeks ago, and @lhoestq referred me to the fix (#2122).
However, the problem seems to persist. Here is the profiled results:
1) Running with 60GB
```
Action ... | [
-0.4040595293045044,
0.21396160125732422,
-0.13430970907211304,
0.1632939577102661,
0.22690027952194214,
-0.1808892786502838,
0.21954894065856934,
0.40079015493392944,
0.11868344992399216,
-0.009201504290103912,
-0.30945369601249695,
0.06606557965278625,
0.1796628087759018,
0.1480765193700... |
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