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language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:808728
- loss:Distillation
base_model: answerdotai/ModernBERT-large
datasets:
- lightonai/ms-marco-en-bge
pipeline_tag: sentence-similarity
library_name: PyLate
---
# PyLate model based on answerdotai/ModernBERT-large
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on the [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) <!-- at revision 45bb4654a4d5aaff24dd11d4781fa46d39bf8c13 -->
- **Document Length:** 180 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 31, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
(1): Dense({'in_features': 1024, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'use_residual': False})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
Use this model with PyLate to index and retrieve documents. The index uses [FastPLAID](https://github.com/lightonai/fast-plaid) for efficient similarity search.
#### Indexing documents
Load the ColBERT model and initialize the PLAID index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
# Step 2: Initialize the PLAID index
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.PLAID(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path="pylate_model_id",
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### train
* Dataset: [train](https://huggingface.co/datasets/lightonai/ms-marco-en-bge) at [ad24729](https://huggingface.co/datasets/lightonai/ms-marco-en-bge/tree/ad2472943e41db62b7883838ad7b2dbc7d572b21)
* Size: 808,728 training samples
* Columns: <code>query_id</code>, <code>document_ids</code>, <code>scores</code>, <code>query</code>, and <code>documents</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | document_ids | scores | query | documents |
|:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------|
| type | string | list | list | string | list |
| details | <ul><li>min: 5 tokens</li><li>mean: 5.59 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.51 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> |
* Samples:
| query_id | document_ids | scores | query | documents |
|:--------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>121352</code> | <code>['2259784', '4923159', '40211', '1545154', '8527175', ...]</code> | <code>[0.2343463897705078, 0.639204204082489, 0.3806908428668976, 0.5623092651367188, 0.8051995635032654, ...]</code> | <code>define extreme</code> | <code>['extremist. 1 AN EXTREMIST IS SOMEONE WHO SUPPORTS AN IDEA, CAUSE, OR SET OF VALUES SO ADAMANTLY AND WITHOUT COMPROMISE THAT SAID PERSON WILL USE THEIR IDEAS TO JUSTIFY ANYTHING THEY DO.', "at the extreme end meaning, at the extreme end definition \| English Cobuild dictionary. extreme. 1 adj Extreme means very great in degree or intensity. The girls were afraid of snakes and picked their way along with extreme caution., ...people living in extreme poverty., ...the author's extreme reluctance to generalise.", 'extremity (plural extremities) 1 The most extreme or furthest point of something. 2 An extreme measure. 3 A hand or foot. A limb (major appendage of human or animal such as a leg an arm or a wing)', ': extreme in a way that is not normal or that shows an illness or mental problem. medical: relating to or caused by disease.: of or relating to the study of diseases: relating to pathology. extreme in a way that is not normal or that shows an illness or mental problem. medical: ...</code> |
| <code>634306</code> | <code>['7723525', '1874779', '379307', '2738583', '7599583', ...]</code> | <code>[0.7124203443527222, 0.7379189729690552, 0.5786551237106323, 0.6142299175262451, 0.6755089163780212, ...]</code> | <code>what does chattel mean on credit history</code> | <code>["Duhaime's Law Dictionary. Chattel Mortgage Definition: Related Terms: Chattel, Mortgage. When a lien is given on goods, chattels, moveable or personal property (other than real property in which case it is referred to as just a mortgage), in writing, to guarantee the payment of a debt or the execution of some action.", 'From Wikipedia, the free encyclopedia. Chattel mortgage, sometimes abbreviated CM, is the legal term for a type of loan contract used in some states with legal systems derived from English law. Under a typical chattel mortgage, the purchaser borrows funds for the purchase of movable personal property (the chattel) from the lender. The lender then secures the loan with a mortgage over the chattel.', 'Chattel Mortgages In Detail. A Chattel Mortgage uses your vehicle or some other (non-real estate) property as the security on the loan meaning you can access a low interest rate.ncidentally, these loans can be used for other purposes such as business equipment. If you have...</code> |
| <code>920825</code> | <code>['5976297', '2866112', '3560294', '3285659', '4706740', ...]</code> | <code>[0.6462352871894836, 0.7880821228027344, 0.791019856929779, 0.7709633111953735, 0.8284491300582886, ...]</code> | <code>what was the great leap forward brainly</code> | <code>['It was a clever scheme that was hatched soon after the 1949 revolution. The first phase was to send spies to the west during the great leap forward in the 1950s to plant falsified basic science into our western understanding of physics.', 'Great Leap Forward Devolution Into the Great Famine . Yang Jisheng, the author of Tombstone , wrote in the New York Times, â\x80\x9cThe Great Leap Forward that Mao began in 1958 set ambitious goals without the means to meet them. A vicious cycle ensued; exaggerated production reports from below emboldened the higher-ups to set even loftier targets.', 'In 1958 Mao introduced a second five year plan which became known as the â\x80\x98Great Leap Forwardâ\x80\x99 (GLF). He believed it was possible for China to overtake Britain as a leading industrial power within seven years and the USA soon after.n 1958 Mao introduced a second five year plan which became known as the â\x80\x98Great Leap Forwardâ\x80\x99 (GLF). He believed it was possible for China to ...</code> |
* Loss: <code>pylate.losses.distillation.Distillation</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 4
- `learning_rate`: 3e-05
- `max_grad_norm`: 2.0
- `num_train_epochs`: 1
- `warmup_ratio`: 0.05
- `bf16`: True
- `dataloader_num_workers`: 4
- `ddp_find_unused_parameters`: False
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 2.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: False
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0010 | 50 | 0.0584 |
| 0.0020 | 100 | 0.0496 |
| 0.0030 | 150 | 0.0494 |
| 0.0040 | 200 | 0.0484 |
| 0.0049 | 250 | 0.048 |
| 0.0059 | 300 | 0.0478 |
| 0.0069 | 350 | 0.0487 |
| 0.0079 | 400 | 0.0481 |
| 0.0089 | 450 | 0.0474 |
| 0.0099 | 500 | 0.0476 |
| 0.0109 | 550 | 0.0475 |
| 0.0119 | 600 | 0.0478 |
| 0.0129 | 650 | 0.0463 |
| 0.0138 | 700 | 0.0476 |
| 0.0148 | 750 | 0.0479 |
| 0.0158 | 800 | 0.0475 |
| 0.0168 | 850 | 0.0469 |
| 0.0178 | 900 | 0.0461 |
| 0.0188 | 950 | 0.0475 |
| 0.0198 | 1000 | 0.0473 |
| 0.0208 | 1050 | 0.047 |
| 0.0218 | 1100 | 0.047 |
| 0.0228 | 1150 | 0.0465 |
| 0.0237 | 1200 | 0.0454 |
| 0.0247 | 1250 | 0.0466 |
| 0.0257 | 1300 | 0.0469 |
| 0.0267 | 1350 | 0.0462 |
| 0.0277 | 1400 | 0.0464 |
| 0.0287 | 1450 | 0.046 |
| 0.0297 | 1500 | 0.0501 |
| 0.0307 | 1550 | 0.0469 |
| 0.0317 | 1600 | 0.0473 |
| 0.0326 | 1650 | 0.0456 |
| 0.0336 | 1700 | 0.0465 |
| 0.0346 | 1750 | 0.0462 |
| 0.0356 | 1800 | 0.0471 |
| 0.0366 | 1850 | 0.0458 |
| 0.0376 | 1900 | 0.0462 |
| 0.0386 | 1950 | 0.0465 |
| 0.0396 | 2000 | 0.0456 |
| 0.0406 | 2050 | 0.0471 |
| 0.0415 | 2100 | 0.0482 |
| 0.0425 | 2150 | 0.0454 |
| 0.0435 | 2200 | 0.0456 |
| 0.0445 | 2250 | 0.0446 |
| 0.0455 | 2300 | 0.0378 |
| 0.0465 | 2350 | 0.0349 |
| 0.0475 | 2400 | 0.0318 |
| 0.0485 | 2450 | 0.0321 |
| 0.0495 | 2500 | 0.0315 |
| 0.0505 | 2550 | 0.0302 |
| 0.0514 | 2600 | 0.0304 |
| 0.0524 | 2650 | 0.0291 |
| 0.0534 | 2700 | 0.0294 |
| 0.0544 | 2750 | 0.0287 |
| 0.0554 | 2800 | 0.0278 |
| 0.0564 | 2850 | 0.0281 |
| 0.0574 | 2900 | 0.0285 |
| 0.0584 | 2950 | 0.0279 |
| 0.0594 | 3000 | 0.0264 |
| 0.0603 | 3050 | 0.0268 |
| 0.0613 | 3100 | 0.0265 |
| 0.0623 | 3150 | 0.0266 |
| 0.0633 | 3200 | 0.0265 |
| 0.0643 | 3250 | 0.0272 |
| 0.0653 | 3300 | 0.0267 |
| 0.0663 | 3350 | 0.0269 |
| 0.0673 | 3400 | 0.0265 |
| 0.0683 | 3450 | 0.0263 |
| 0.0692 | 3500 | 0.0253 |
| 0.0702 | 3550 | 0.0257 |
| 0.0712 | 3600 | 0.0258 |
| 0.0722 | 3650 | 0.0253 |
| 0.0732 | 3700 | 0.0256 |
| 0.0742 | 3750 | 0.0255 |
| 0.0752 | 3800 | 0.0248 |
| 0.0762 | 3850 | 0.0247 |
| 0.0772 | 3900 | 0.026 |
| 0.0781 | 3950 | 0.0248 |
| 0.0791 | 4000 | 0.0251 |
| 0.0801 | 4050 | 0.0242 |
| 0.0811 | 4100 | 0.0251 |
| 0.0821 | 4150 | 0.0246 |
| 0.0831 | 4200 | 0.0242 |
| 0.0841 | 4250 | 0.0241 |
| 0.0851 | 4300 | 0.0243 |
| 0.0861 | 4350 | 0.0244 |
| 0.0871 | 4400 | 0.0251 |
| 0.0880 | 4450 | 0.0246 |
| 0.0890 | 4500 | 0.0232 |
| 0.0900 | 4550 | 0.024 |
| 0.0910 | 4600 | 0.0235 |
| 0.0920 | 4650 | 0.0245 |
| 0.0930 | 4700 | 0.0228 |
| 0.0940 | 4750 | 0.0239 |
| 0.0950 | 4800 | 0.0241 |
| 0.0960 | 4850 | 0.0239 |
| 0.0969 | 4900 | 0.0228 |
| 0.0979 | 4950 | 0.0233 |
| 0.0989 | 5000 | 0.0233 |
| 0.0999 | 5050 | 0.0236 |
| 0.1009 | 5100 | 0.024 |
| 0.1019 | 5150 | 0.0232 |
| 0.1029 | 5200 | 0.0237 |
| 0.1039 | 5250 | 0.0229 |
| 0.1049 | 5300 | 0.0236 |
| 0.1058 | 5350 | 0.0234 |
| 0.1068 | 5400 | 0.0224 |
| 0.1078 | 5450 | 0.0227 |
| 0.1088 | 5500 | 0.024 |
| 0.1098 | 5550 | 0.0233 |
| 0.1108 | 5600 | 0.023 |
| 0.1118 | 5650 | 0.0221 |
| 0.1128 | 5700 | 0.0227 |
| 0.1138 | 5750 | 0.0235 |
| 0.1147 | 5800 | 0.0228 |
| 0.1157 | 5850 | 0.0228 |
| 0.1167 | 5900 | 0.0231 |
| 0.1177 | 5950 | 0.022 |
| 0.1187 | 6000 | 0.0225 |
| 0.1197 | 6050 | 0.0214 |
| 0.1207 | 6100 | 0.0218 |
| 0.1217 | 6150 | 0.0221 |
| 0.1227 | 6200 | 0.0222 |
| 0.1237 | 6250 | 0.0221 |
| 0.1246 | 6300 | 0.0214 |
| 0.1256 | 6350 | 0.0225 |
| 0.1266 | 6400 | 0.0219 |
| 0.1276 | 6450 | 0.0218 |
| 0.1286 | 6500 | 0.0223 |
| 0.1296 | 6550 | 0.0216 |
| 0.1306 | 6600 | 0.0206 |
| 0.1316 | 6650 | 0.0215 |
| 0.1326 | 6700 | 0.0223 |
| 0.1335 | 6750 | 0.0222 |
| 0.1345 | 6800 | 0.0212 |
| 0.1355 | 6850 | 0.0213 |
| 0.1365 | 6900 | 0.0212 |
| 0.1375 | 6950 | 0.0206 |
| 0.1385 | 7000 | 0.0204 |
| 0.1395 | 7050 | 0.0212 |
| 0.1405 | 7100 | 0.0214 |
| 0.1415 | 7150 | 0.0218 |
| 0.1424 | 7200 | 0.0212 |
| 0.1434 | 7250 | 0.0199 |
| 0.1444 | 7300 | 0.0215 |
| 0.1454 | 7350 | 0.0215 |
| 0.1464 | 7400 | 0.0212 |
| 0.1474 | 7450 | 0.0211 |
| 0.1484 | 7500 | 0.0208 |
| 0.1494 | 7550 | 0.0211 |
| 0.1504 | 7600 | 0.0219 |
| 0.1514 | 7650 | 0.0207 |
| 0.1523 | 7700 | 0.0204 |
| 0.1533 | 7750 | 0.0209 |
| 0.1543 | 7800 | 0.0209 |
| 0.1553 | 7850 | 0.0206 |
| 0.1563 | 7900 | 0.0206 |
| 0.1573 | 7950 | 0.0204 |
| 0.1583 | 8000 | 0.0206 |
| 0.1593 | 8050 | 0.0199 |
| 0.1603 | 8100 | 0.0197 |
| 0.1612 | 8150 | 0.0206 |
| 0.1622 | 8200 | 0.0201 |
| 0.1632 | 8250 | 0.0191 |
| 0.1642 | 8300 | 0.0201 |
| 0.1652 | 8350 | 0.0211 |
| 0.1662 | 8400 | 0.02 |
| 0.1672 | 8450 | 0.0201 |
| 0.1682 | 8500 | 0.0194 |
| 0.1692 | 8550 | 0.0205 |
| 0.1701 | 8600 | 0.0199 |
| 0.1711 | 8650 | 0.0202 |
| 0.1721 | 8700 | 0.02 |
| 0.1731 | 8750 | 0.0205 |
| 0.1741 | 8800 | 0.0193 |
| 0.1751 | 8850 | 0.02 |
| 0.1761 | 8900 | 0.0191 |
| 0.1771 | 8950 | 0.0195 |
| 0.1781 | 9000 | 0.0201 |
| 0.1790 | 9050 | 0.0192 |
| 0.1800 | 9100 | 0.0203 |
| 0.1810 | 9150 | 0.0191 |
| 0.1820 | 9200 | 0.0207 |
| 0.1830 | 9250 | 0.0205 |
| 0.1840 | 9300 | 0.0192 |
| 0.1850 | 9350 | 0.0193 |
| 0.1860 | 9400 | 0.0193 |
| 0.1870 | 9450 | 0.0189 |
| 0.1880 | 9500 | 0.0196 |
| 0.1889 | 9550 | 0.019 |
| 0.1899 | 9600 | 0.0199 |
| 0.1909 | 9650 | 0.0191 |
| 0.1919 | 9700 | 0.0189 |
| 0.1929 | 9750 | 0.0184 |
| 0.1939 | 9800 | 0.0185 |
| 0.1949 | 9850 | 0.0181 |
| 0.1959 | 9900 | 0.0183 |
| 0.1969 | 9950 | 0.0187 |
| 0.1978 | 10000 | 0.0187 |
| 0.1988 | 10050 | 0.0188 |
| 0.1998 | 10100 | 0.0181 |
| 0.2008 | 10150 | 0.0183 |
| 0.2018 | 10200 | 0.0179 |
| 0.2028 | 10250 | 0.019 |
| 0.2038 | 10300 | 0.0186 |
| 0.2048 | 10350 | 0.0187 |
| 0.2058 | 10400 | 0.0183 |
| 0.2067 | 10450 | 0.0181 |
| 0.2077 | 10500 | 0.0184 |
| 0.2087 | 10550 | 0.018 |
| 0.2097 | 10600 | 0.0188 |
| 0.2107 | 10650 | 0.0185 |
| 0.2117 | 10700 | 0.0185 |
| 0.2127 | 10750 | 0.0176 |
| 0.2137 | 10800 | 0.0176 |
| 0.2147 | 10850 | 0.018 |
| 0.2156 | 10900 | 0.0181 |
| 0.2166 | 10950 | 0.0177 |
| 0.2176 | 11000 | 0.0181 |
| 0.2186 | 11050 | 0.018 |
| 0.2196 | 11100 | 0.0187 |
| 0.2206 | 11150 | 0.0173 |
| 0.2216 | 11200 | 0.0171 |
| 0.2226 | 11250 | 0.0184 |
| 0.2236 | 11300 | 0.0173 |
| 0.2246 | 11350 | 0.0178 |
| 0.2255 | 11400 | 0.0173 |
| 0.2265 | 11450 | 0.0177 |
| 0.2275 | 11500 | 0.0171 |
| 0.2285 | 11550 | 0.0171 |
| 0.2295 | 11600 | 0.0175 |
| 0.2305 | 11650 | 0.0169 |
| 0.2315 | 11700 | 0.0164 |
| 0.2325 | 11750 | 0.0163 |
| 0.2335 | 11800 | 0.0177 |
| 0.2344 | 11850 | 0.017 |
| 0.2354 | 11900 | 0.0164 |
| 0.2364 | 11950 | 0.017 |
| 0.2374 | 12000 | 0.0176 |
| 0.2384 | 12050 | 0.0167 |
| 0.2394 | 12100 | 0.0163 |
| 0.2404 | 12150 | 0.0168 |
| 0.2414 | 12200 | 0.0161 |
| 0.2424 | 12250 | 0.0169 |
| 0.2433 | 12300 | 0.0171 |
| 0.2443 | 12350 | 0.0163 |
| 0.2453 | 12400 | 0.0167 |
| 0.2463 | 12450 | 0.0162 |
| 0.2473 | 12500 | 0.0156 |
| 0.2483 | 12550 | 0.0159 |
| 0.2493 | 12600 | 0.0161 |
| 0.2503 | 12650 | 0.0161 |
| 0.2513 | 12700 | 0.0163 |
| 0.2523 | 12750 | 0.0156 |
| 0.2532 | 12800 | 0.0158 |
| 0.2542 | 12850 | 0.0157 |
| 0.2552 | 12900 | 0.0159 |
| 0.2562 | 12950 | 0.0159 |
| 0.2572 | 13000 | 0.0159 |
| 0.2582 | 13050 | 0.0155 |
| 0.2592 | 13100 | 0.0152 |
| 0.2602 | 13150 | 0.0151 |
| 0.2612 | 13200 | 0.0151 |
| 0.2621 | 13250 | 0.0143 |
| 0.2631 | 13300 | 0.0151 |
| 0.2641 | 13350 | 0.0151 |
| 0.2651 | 13400 | 0.0152 |
| 0.2661 | 13450 | 0.0147 |
| 0.2671 | 13500 | 0.0147 |
| 0.2681 | 13550 | 0.015 |
| 0.2691 | 13600 | 0.015 |
| 0.2701 | 13650 | 0.0145 |
| 0.2710 | 13700 | 0.0156 |
| 0.2720 | 13750 | 0.0151 |
| 0.2730 | 13800 | 0.0143 |
| 0.2740 | 13850 | 0.0142 |
| 0.2750 | 13900 | 0.0147 |
| 0.2760 | 13950 | 0.0145 |
| 0.2770 | 14000 | 0.0141 |
| 0.2780 | 14050 | 0.0138 |
| 0.2790 | 14100 | 0.0145 |
| 0.2799 | 14150 | 0.0144 |
| 0.2809 | 14200 | 0.0142 |
| 0.2819 | 14250 | 0.0139 |
| 0.2829 | 14300 | 0.0143 |
| 0.2839 | 14350 | 0.0139 |
| 0.2849 | 14400 | 0.0136 |
| 0.2859 | 14450 | 0.015 |
| 0.2869 | 14500 | 0.0133 |
| 0.2879 | 14550 | 0.0142 |
| 0.2889 | 14600 | 0.0137 |
| 0.2898 | 14650 | 0.0139 |
| 0.2908 | 14700 | 0.014 |
| 0.2918 | 14750 | 0.0134 |
| 0.2928 | 14800 | 0.0138 |
| 0.2938 | 14850 | 0.0134 |
| 0.2948 | 14900 | 0.014 |
| 0.2958 | 14950 | 0.0133 |
| 0.2968 | 15000 | 0.0138 |
| 0.2978 | 15050 | 0.0132 |
| 0.2987 | 15100 | 0.0131 |
| 0.2997 | 15150 | 0.0134 |
| 0.3007 | 15200 | 0.0142 |
| 0.3017 | 15250 | 0.0128 |
| 0.3027 | 15300 | 0.0134 |
| 0.3037 | 15350 | 0.013 |
| 0.3047 | 15400 | 0.0132 |
| 0.3057 | 15450 | 0.0134 |
| 0.3067 | 15500 | 0.0128 |
| 0.3076 | 15550 | 0.0136 |
| 0.3086 | 15600 | 0.0134 |
| 0.3096 | 15650 | 0.013 |
| 0.3106 | 15700 | 0.0128 |
| 0.3116 | 15750 | 0.0133 |
| 0.3126 | 15800 | 0.0132 |
| 0.3136 | 15850 | 0.0128 |
| 0.3146 | 15900 | 0.0124 |
| 0.3156 | 15950 | 0.0131 |
| 0.3165 | 16000 | 0.0128 |
| 0.3175 | 16050 | 0.0125 |
| 0.3185 | 16100 | 0.0126 |
| 0.3195 | 16150 | 0.0127 |
| 0.3205 | 16200 | 0.0124 |
| 0.3215 | 16250 | 0.0131 |
| 0.3225 | 16300 | 0.0122 |
| 0.3235 | 16350 | 0.0129 |
| 0.3245 | 16400 | 0.0126 |
| 0.3255 | 16450 | 0.0125 |
| 0.3264 | 16500 | 0.0122 |
| 0.3274 | 16550 | 0.0123 |
| 0.3284 | 16600 | 0.0123 |
| 0.3294 | 16650 | 0.0126 |
| 0.3304 | 16700 | 0.0123 |
| 0.3314 | 16750 | 0.0135 |
| 0.3324 | 16800 | 0.0125 |
| 0.3334 | 16850 | 0.0121 |
| 0.3344 | 16900 | 0.012 |
| 0.3353 | 16950 | 0.0125 |
| 0.3363 | 17000 | 0.0121 |
| 0.3373 | 17050 | 0.0118 |
| 0.3383 | 17100 | 0.0123 |
| 0.3393 | 17150 | 0.0115 |
| 0.3403 | 17200 | 0.0126 |
| 0.3413 | 17250 | 0.0115 |
| 0.3423 | 17300 | 0.0115 |
| 0.3433 | 17350 | 0.0119 |
| 0.3442 | 17400 | 0.0121 |
| 0.3452 | 17450 | 0.0116 |
| 0.3462 | 17500 | 0.0119 |
| 0.3472 | 17550 | 0.0116 |
| 0.3482 | 17600 | 0.0116 |
| 0.3492 | 17650 | 0.0119 |
| 0.3502 | 17700 | 0.0118 |
| 0.3512 | 17750 | 0.0119 |
| 0.3522 | 17800 | 0.0116 |
| 0.3532 | 17850 | 0.0119 |
| 0.3541 | 17900 | 0.0118 |
| 0.3551 | 17950 | 0.0117 |
| 0.3561 | 18000 | 0.0113 |
| 0.3571 | 18050 | 0.0118 |
| 0.3581 | 18100 | 0.0117 |
| 0.3591 | 18150 | 0.0114 |
| 0.3601 | 18200 | 0.0118 |
| 0.3611 | 18250 | 0.0113 |
| 0.3621 | 18300 | 0.0113 |
| 0.3630 | 18350 | 0.0116 |
| 0.3640 | 18400 | 0.0124 |
| 0.3650 | 18450 | 0.0115 |
| 0.3660 | 18500 | 0.0115 |
| 0.3670 | 18550 | 0.011 |
| 0.3680 | 18600 | 0.0115 |
| 0.3690 | 18650 | 0.0115 |
| 0.3700 | 18700 | 0.011 |
| 0.3710 | 18750 | 0.0112 |
| 0.3719 | 18800 | 0.0113 |
| 0.3729 | 18850 | 0.0113 |
| 0.3739 | 18900 | 0.0115 |
| 0.3749 | 18950 | 0.0109 |
| 0.3759 | 19000 | 0.0112 |
| 0.3769 | 19050 | 0.0112 |
| 0.3779 | 19100 | 0.011 |
| 0.3789 | 19150 | 0.0116 |
| 0.3799 | 19200 | 0.0115 |
| 0.3808 | 19250 | 0.0114 |
| 0.3818 | 19300 | 0.011 |
| 0.3828 | 19350 | 0.0112 |
| 0.3838 | 19400 | 0.011 |
| 0.3848 | 19450 | 0.0107 |
| 0.3858 | 19500 | 0.0113 |
| 0.3868 | 19550 | 0.0115 |
| 0.3878 | 19600 | 0.0113 |
| 0.3888 | 19650 | 0.0112 |
| 0.3898 | 19700 | 0.0114 |
| 0.3907 | 19750 | 0.0111 |
| 0.3917 | 19800 | 0.0107 |
| 0.3927 | 19850 | 0.0108 |
| 0.3937 | 19900 | 0.0112 |
| 0.3947 | 19950 | 0.011 |
| 0.3957 | 20000 | 0.0111 |
| 0.3967 | 20050 | 0.0108 |
| 0.3977 | 20100 | 0.0107 |
| 0.3987 | 20150 | 0.0104 |
| 0.3996 | 20200 | 0.0104 |
| 0.4006 | 20250 | 0.0104 |
| 0.4016 | 20300 | 0.0109 |
| 0.4026 | 20350 | 0.011 |
| 0.4036 | 20400 | 0.0109 |
| 0.4046 | 20450 | 0.0107 |
| 0.4056 | 20500 | 0.011 |
| 0.4066 | 20550 | 0.0108 |
| 0.4076 | 20600 | 0.0109 |
| 0.4085 | 20650 | 0.0105 |
| 0.4095 | 20700 | 0.0107 |
| 0.4105 | 20750 | 0.0106 |
| 0.4115 | 20800 | 0.0106 |
| 0.4125 | 20850 | 0.0106 |
| 0.4135 | 20900 | 0.011 |
| 0.4145 | 20950 | 0.0108 |
| 0.4155 | 21000 | 0.0102 |
| 0.4165 | 21050 | 0.0106 |
| 0.4174 | 21100 | 0.0103 |
| 0.4184 | 21150 | 0.0108 |
| 0.4194 | 21200 | 0.0098 |
| 0.4204 | 21250 | 0.0106 |
| 0.4214 | 21300 | 0.0101 |
| 0.4224 | 21350 | 0.0111 |
| 0.4234 | 21400 | 0.0103 |
| 0.4244 | 21450 | 0.0104 |
| 0.4254 | 21500 | 0.0103 |
| 0.4264 | 21550 | 0.0103 |
| 0.4273 | 21600 | 0.0099 |
| 0.4283 | 21650 | 0.01 |
| 0.4293 | 21700 | 0.0099 |
| 0.4303 | 21750 | 0.01 |
| 0.4313 | 21800 | 0.0104 |
| 0.4323 | 21850 | 0.0103 |
| 0.4333 | 21900 | 0.0105 |
| 0.4343 | 21950 | 0.0103 |
| 0.4353 | 22000 | 0.01 |
| 0.4362 | 22050 | 0.0102 |
| 0.4372 | 22100 | 0.0103 |
| 0.4382 | 22150 | 0.0102 |
| 0.4392 | 22200 | 0.01 |
| 0.4402 | 22250 | 0.0106 |
| 0.4412 | 22300 | 0.0101 |
| 0.4422 | 22350 | 0.01 |
| 0.4432 | 22400 | 0.0098 |
| 0.4442 | 22450 | 0.0106 |
| 0.4451 | 22500 | 0.01 |
| 0.4461 | 22550 | 0.0103 |
| 0.4471 | 22600 | 0.0101 |
| 0.4481 | 22650 | 0.0103 |
| 0.4491 | 22700 | 0.0099 |
| 0.4501 | 22750 | 0.0105 |
| 0.4511 | 22800 | 0.0096 |
| 0.4521 | 22850 | 0.0099 |
| 0.4531 | 22900 | 0.01 |
| 0.4541 | 22950 | 0.01 |
| 0.4550 | 23000 | 0.0101 |
| 0.4560 | 23050 | 0.0095 |
| 0.4570 | 23100 | 0.0101 |
| 0.4580 | 23150 | 0.0099 |
| 0.4590 | 23200 | 0.01 |
| 0.4600 | 23250 | 0.0097 |
| 0.4610 | 23300 | 0.0097 |
| 0.4620 | 23350 | 0.0098 |
| 0.4630 | 23400 | 0.0097 |
| 0.4639 | 23450 | 0.0096 |
| 0.4649 | 23500 | 0.0099 |
| 0.4659 | 23550 | 0.0097 |
| 0.4669 | 23600 | 0.0095 |
| 0.4679 | 23650 | 0.0098 |
| 0.4689 | 23700 | 0.0096 |
| 0.4699 | 23750 | 0.0098 |
| 0.4709 | 23800 | 0.0094 |
| 0.4719 | 23850 | 0.0093 |
| 0.4728 | 23900 | 0.0094 |
| 0.4738 | 23950 | 0.0092 |
| 0.4748 | 24000 | 0.0096 |
| 0.4758 | 24050 | 0.0095 |
| 0.4768 | 24100 | 0.01 |
| 0.4778 | 24150 | 0.01 |
| 0.4788 | 24200 | 0.0103 |
| 0.4798 | 24250 | 0.0094 |
| 0.4808 | 24300 | 0.0097 |
| 0.4817 | 24350 | 0.0096 |
| 0.4827 | 24400 | 0.0096 |
| 0.4837 | 24450 | 0.0099 |
| 0.4847 | 24500 | 0.0099 |
| 0.4857 | 24550 | 0.0092 |
| 0.4867 | 24600 | 0.0092 |
| 0.4877 | 24650 | 0.0097 |
| 0.4887 | 24700 | 0.0096 |
| 0.4897 | 24750 | 0.0096 |
| 0.4907 | 24800 | 0.01 |
| 0.4916 | 24850 | 0.0096 |
| 0.4926 | 24900 | 0.0098 |
| 0.4936 | 24950 | 0.0094 |
| 0.4946 | 25000 | 0.0096 |
| 0.4956 | 25050 | 0.0099 |
| 0.4966 | 25100 | 0.0094 |
| 0.4976 | 25150 | 0.0094 |
| 0.4986 | 25200 | 0.0103 |
| 0.4996 | 25250 | 0.0098 |
| 0.5005 | 25300 | 0.0096 |
| 0.5015 | 25350 | 0.0099 |
| 0.5025 | 25400 | 0.0092 |
| 0.5035 | 25450 | 0.0099 |
| 0.5045 | 25500 | 0.0092 |
| 0.5055 | 25550 | 0.0094 |
| 0.5065 | 25600 | 0.0094 |
| 0.5075 | 25650 | 0.0093 |
| 0.5085 | 25700 | 0.0093 |
| 0.5094 | 25750 | 0.0095 |
| 0.5104 | 25800 | 0.0086 |
| 0.5114 | 25850 | 0.009 |
| 0.5124 | 25900 | 0.0094 |
| 0.5134 | 25950 | 0.0091 |
| 0.5144 | 26000 | 0.0091 |
| 0.5154 | 26050 | 0.0089 |
| 0.5164 | 26100 | 0.0095 |
| 0.5174 | 26150 | 0.0092 |
| 0.5183 | 26200 | 0.0091 |
| 0.5193 | 26250 | 0.0092 |
| 0.5203 | 26300 | 0.009 |
| 0.5213 | 26350 | 0.0091 |
| 0.5223 | 26400 | 0.0089 |
| 0.5233 | 26450 | 0.0092 |
| 0.5243 | 26500 | 0.0088 |
| 0.5253 | 26550 | 0.0093 |
| 0.5263 | 26600 | 0.0093 |
| 0.5273 | 26650 | 0.0097 |
| 0.5282 | 26700 | 0.0092 |
| 0.5292 | 26750 | 0.0091 |
| 0.5302 | 26800 | 0.0091 |
| 0.5312 | 26850 | 0.0088 |
| 0.5322 | 26900 | 0.0089 |
| 0.5332 | 26950 | 0.0093 |
| 0.5342 | 27000 | 0.009 |
| 0.5352 | 27050 | 0.0091 |
| 0.5362 | 27100 | 0.0092 |
| 0.5371 | 27150 | 0.0093 |
| 0.5381 | 27200 | 0.0092 |
| 0.5391 | 27250 | 0.0091 |
| 0.5401 | 27300 | 0.0087 |
| 0.5411 | 27350 | 0.0091 |
| 0.5421 | 27400 | 0.009 |
| 0.5431 | 27450 | 0.0088 |
| 0.5441 | 27500 | 0.0089 |
| 0.5451 | 27550 | 0.0091 |
| 0.5460 | 27600 | 0.0091 |
| 0.5470 | 27650 | 0.0088 |
| 0.5480 | 27700 | 0.0088 |
| 0.5490 | 27750 | 0.0092 |
| 0.5500 | 27800 | 0.0094 |
| 0.5510 | 27850 | 0.0087 |
| 0.5520 | 27900 | 0.0092 |
| 0.5530 | 27950 | 0.0088 |
| 0.5540 | 28000 | 0.0093 |
| 0.5550 | 28050 | 0.0089 |
| 0.5559 | 28100 | 0.009 |
| 0.5569 | 28150 | 0.0088 |
| 0.5579 | 28200 | 0.0082 |
| 0.5589 | 28250 | 0.0087 |
| 0.5599 | 28300 | 0.0086 |
| 0.5609 | 28350 | 0.0094 |
| 0.5619 | 28400 | 0.0089 |
| 0.5629 | 28450 | 0.0089 |
| 0.5639 | 28500 | 0.009 |
| 0.5648 | 28550 | 0.0086 |
| 0.5658 | 28600 | 0.0089 |
| 0.5668 | 28650 | 0.0091 |
| 0.5678 | 28700 | 0.0092 |
| 0.5688 | 28750 | 0.0089 |
| 0.5698 | 28800 | 0.0085 |
| 0.5708 | 28850 | 0.0088 |
| 0.5718 | 28900 | 0.0093 |
| 0.5728 | 28950 | 0.0086 |
| 0.5737 | 29000 | 0.0093 |
| 0.5747 | 29050 | 0.0089 |
| 0.5757 | 29100 | 0.0087 |
| 0.5767 | 29150 | 0.0086 |
| 0.5777 | 29200 | 0.0084 |
| 0.5787 | 29250 | 0.0086 |
| 0.5797 | 29300 | 0.0085 |
| 0.5807 | 29350 | 0.0084 |
| 0.5817 | 29400 | 0.0086 |
| 0.5826 | 29450 | 0.0086 |
| 0.5836 | 29500 | 0.0089 |
| 0.5846 | 29550 | 0.0085 |
| 0.5856 | 29600 | 0.0086 |
| 0.5866 | 29650 | 0.0087 |
| 0.5876 | 29700 | 0.0085 |
| 0.5886 | 29750 | 0.0091 |
| 0.5896 | 29800 | 0.0084 |
| 0.5906 | 29850 | 0.0086 |
| 0.5916 | 29900 | 0.0087 |
| 0.5925 | 29950 | 0.0088 |
| 0.5935 | 30000 | 0.0086 |
| 0.5945 | 30050 | 0.009 |
| 0.5955 | 30100 | 0.0085 |
| 0.5965 | 30150 | 0.0084 |
| 0.5975 | 30200 | 0.0086 |
| 0.5985 | 30250 | 0.0083 |
| 0.5995 | 30300 | 0.0085 |
| 0.6005 | 30350 | 0.0087 |
| 0.6014 | 30400 | 0.0086 |
| 0.6024 | 30450 | 0.0088 |
| 0.6034 | 30500 | 0.0084 |
| 0.6044 | 30550 | 0.0086 |
| 0.6054 | 30600 | 0.0088 |
| 0.6064 | 30650 | 0.0085 |
| 0.6074 | 30700 | 0.0084 |
| 0.6084 | 30750 | 0.0085 |
| 0.6094 | 30800 | 0.0084 |
| 0.6103 | 30850 | 0.0084 |
| 0.6113 | 30900 | 0.0087 |
| 0.6123 | 30950 | 0.0085 |
| 0.6133 | 31000 | 0.0087 |
| 0.6143 | 31050 | 0.0087 |
| 0.6153 | 31100 | 0.0084 |
| 0.6163 | 31150 | 0.0085 |
| 0.6173 | 31200 | 0.0085 |
| 0.6183 | 31250 | 0.0085 |
| 0.6193 | 31300 | 0.008 |
| 0.6202 | 31350 | 0.0083 |
| 0.6212 | 31400 | 0.0085 |
| 0.6222 | 31450 | 0.0084 |
| 0.6232 | 31500 | 0.0081 |
| 0.6242 | 31550 | 0.0087 |
| 0.6252 | 31600 | 0.0081 |
| 0.6262 | 31650 | 0.0081 |
| 0.6272 | 31700 | 0.0085 |
| 0.6282 | 31750 | 0.0083 |
| 0.6291 | 31800 | 0.0086 |
| 0.6301 | 31850 | 0.0089 |
| 0.6311 | 31900 | 0.0082 |
| 0.6321 | 31950 | 0.0084 |
| 0.6331 | 32000 | 0.008 |
| 0.6341 | 32050 | 0.0084 |
| 0.6351 | 32100 | 0.0084 |
| 0.6361 | 32150 | 0.0082 |
| 0.6371 | 32200 | 0.0081 |
| 0.6380 | 32250 | 0.0084 |
| 0.6390 | 32300 | 0.008 |
| 0.6400 | 32350 | 0.008 |
| 0.6410 | 32400 | 0.0083 |
| 0.6420 | 32450 | 0.0083 |
| 0.6430 | 32500 | 0.0082 |
| 0.6440 | 32550 | 0.008 |
| 0.6450 | 32600 | 0.0085 |
| 0.6460 | 32650 | 0.0083 |
| 0.6469 | 32700 | 0.0084 |
| 0.6479 | 32750 | 0.0083 |
| 0.6489 | 32800 | 0.0078 |
| 0.6499 | 32850 | 0.0085 |
| 0.6509 | 32900 | 0.0079 |
| 0.6519 | 32950 | 0.008 |
| 0.6529 | 33000 | 0.008 |
| 0.6539 | 33050 | 0.0082 |
| 0.6549 | 33100 | 0.0077 |
| 0.6559 | 33150 | 0.0082 |
| 0.6568 | 33200 | 0.0086 |
| 0.6578 | 33250 | 0.008 |
| 0.6588 | 33300 | 0.0083 |
| 0.6598 | 33350 | 0.0083 |
| 0.6608 | 33400 | 0.008 |
| 0.6618 | 33450 | 0.008 |
| 0.6628 | 33500 | 0.008 |
| 0.6638 | 33550 | 0.0078 |
| 0.6648 | 33600 | 0.0082 |
| 0.6657 | 33650 | 0.0082 |
| 0.6667 | 33700 | 0.0082 |
| 0.6677 | 33750 | 0.0084 |
| 0.6687 | 33800 | 0.0083 |
| 0.6697 | 33850 | 0.0078 |
| 0.6707 | 33900 | 0.0078 |
| 0.6717 | 33950 | 0.0079 |
| 0.6727 | 34000 | 0.0078 |
| 0.6737 | 34050 | 0.008 |
| 0.6746 | 34100 | 0.008 |
| 0.6756 | 34150 | 0.0073 |
| 0.6766 | 34200 | 0.0079 |
| 0.6776 | 34250 | 0.0083 |
| 0.6786 | 34300 | 0.0077 |
| 0.6796 | 34350 | 0.0082 |
| 0.6806 | 34400 | 0.008 |
| 0.6816 | 34450 | 0.0083 |
| 0.6826 | 34500 | 0.0078 |
| 0.6835 | 34550 | 0.0077 |
| 0.6845 | 34600 | 0.0081 |
| 0.6855 | 34650 | 0.0079 |
| 0.6865 | 34700 | 0.0077 |
| 0.6875 | 34750 | 0.0077 |
| 0.6885 | 34800 | 0.0076 |
| 0.6895 | 34850 | 0.0078 |
| 0.6905 | 34900 | 0.0076 |
| 0.6915 | 34950 | 0.008 |
| 0.6925 | 35000 | 0.008 |
| 0.6934 | 35050 | 0.0077 |
| 0.6944 | 35100 | 0.0079 |
| 0.6954 | 35150 | 0.0076 |
| 0.6964 | 35200 | 0.0077 |
| 0.6974 | 35250 | 0.0082 |
| 0.6984 | 35300 | 0.008 |
| 0.6994 | 35350 | 0.0079 |
| 0.7004 | 35400 | 0.0077 |
| 0.7014 | 35450 | 0.0076 |
| 0.7023 | 35500 | 0.0082 |
| 0.7033 | 35550 | 0.0079 |
| 0.7043 | 35600 | 0.0077 |
| 0.7053 | 35650 | 0.0079 |
| 0.7063 | 35700 | 0.008 |
| 0.7073 | 35750 | 0.0082 |
| 0.7083 | 35800 | 0.0077 |
| 0.7093 | 35850 | 0.0076 |
| 0.7103 | 35900 | 0.0082 |
| 0.7112 | 35950 | 0.0082 |
| 0.7122 | 36000 | 0.0078 |
| 0.7132 | 36050 | 0.0081 |
| 0.7142 | 36100 | 0.0081 |
| 0.7152 | 36150 | 0.0078 |
| 0.7162 | 36200 | 0.0079 |
| 0.7172 | 36250 | 0.0076 |
| 0.7182 | 36300 | 0.0078 |
| 0.7192 | 36350 | 0.0076 |
| 0.7202 | 36400 | 0.0078 |
| 0.7211 | 36450 | 0.0078 |
| 0.7221 | 36500 | 0.0079 |
| 0.7231 | 36550 | 0.0075 |
| 0.7241 | 36600 | 0.008 |
| 0.7251 | 36650 | 0.0075 |
| 0.7261 | 36700 | 0.0078 |
| 0.7271 | 36750 | 0.0081 |
| 0.7281 | 36800 | 0.0081 |
| 0.7291 | 36850 | 0.0078 |
| 0.7300 | 36900 | 0.0074 |
| 0.7310 | 36950 | 0.008 |
| 0.7320 | 37000 | 0.0078 |
| 0.7330 | 37050 | 0.0076 |
| 0.7340 | 37100 | 0.0073 |
| 0.7350 | 37150 | 0.0079 |
| 0.7360 | 37200 | 0.0072 |
| 0.7370 | 37250 | 0.008 |
| 0.7380 | 37300 | 0.0077 |
| 0.7389 | 37350 | 0.0077 |
| 0.7399 | 37400 | 0.0079 |
| 0.7409 | 37450 | 0.0075 |
| 0.7419 | 37500 | 0.0082 |
| 0.7429 | 37550 | 0.0077 |
| 0.7439 | 37600 | 0.0077 |
| 0.7449 | 37650 | 0.0075 |
| 0.7459 | 37700 | 0.008 |
| 0.7469 | 37750 | 0.0079 |
| 0.7478 | 37800 | 0.0078 |
| 0.7488 | 37850 | 0.0076 |
| 0.7498 | 37900 | 0.0079 |
| 0.7508 | 37950 | 0.0073 |
| 0.7518 | 38000 | 0.0075 |
| 0.7528 | 38050 | 0.0074 |
| 0.7538 | 38100 | 0.0081 |
| 0.7548 | 38150 | 0.0078 |
| 0.7558 | 38200 | 0.0073 |
| 0.7568 | 38250 | 0.0081 |
| 0.7577 | 38300 | 0.0074 |
| 0.7587 | 38350 | 0.0074 |
| 0.7597 | 38400 | 0.0074 |
| 0.7607 | 38450 | 0.0074 |
| 0.7617 | 38500 | 0.0077 |
| 0.7627 | 38550 | 0.0073 |
| 0.7637 | 38600 | 0.0079 |
| 0.7647 | 38650 | 0.0074 |
| 0.7657 | 38700 | 0.0074 |
| 0.7666 | 38750 | 0.0075 |
| 0.7676 | 38800 | 0.0076 |
| 0.7686 | 38850 | 0.0076 |
| 0.7696 | 38900 | 0.0075 |
| 0.7706 | 38950 | 0.0072 |
| 0.7716 | 39000 | 0.0074 |
| 0.7726 | 39050 | 0.0076 |
| 0.7736 | 39100 | 0.0074 |
| 0.7746 | 39150 | 0.0074 |
| 0.7755 | 39200 | 0.0077 |
| 0.7765 | 39250 | 0.008 |
| 0.7775 | 39300 | 0.0077 |
| 0.7785 | 39350 | 0.0075 |
| 0.7795 | 39400 | 0.0076 |
| 0.7805 | 39450 | 0.0075 |
| 0.7815 | 39500 | 0.0073 |
| 0.7825 | 39550 | 0.0075 |
| 0.7835 | 39600 | 0.0076 |
| 0.7844 | 39650 | 0.0076 |
| 0.7854 | 39700 | 0.0074 |
| 0.7864 | 39750 | 0.0075 |
| 0.7874 | 39800 | 0.0074 |
| 0.7884 | 39850 | 0.0075 |
| 0.7894 | 39900 | 0.0079 |
| 0.7904 | 39950 | 0.0072 |
| 0.7914 | 40000 | 0.0074 |
| 0.7924 | 40050 | 0.0076 |
| 0.7934 | 40100 | 0.0077 |
| 0.7943 | 40150 | 0.0078 |
| 0.7953 | 40200 | 0.0077 |
| 0.7963 | 40250 | 0.0069 |
| 0.7973 | 40300 | 0.0076 |
| 0.7983 | 40350 | 0.0076 |
| 0.7993 | 40400 | 0.0078 |
| 0.8003 | 40450 | 0.0075 |
| 0.8013 | 40500 | 0.0072 |
| 0.8023 | 40550 | 0.0073 |
| 0.8032 | 40600 | 0.0071 |
| 0.8042 | 40650 | 0.0073 |
| 0.8052 | 40700 | 0.0073 |
| 0.8062 | 40750 | 0.0073 |
| 0.8072 | 40800 | 0.0074 |
| 0.8082 | 40850 | 0.007 |
| 0.8092 | 40900 | 0.0075 |
| 0.8102 | 40950 | 0.0075 |
| 0.8112 | 41000 | 0.0073 |
| 0.8121 | 41050 | 0.0078 |
| 0.8131 | 41100 | 0.0074 |
| 0.8141 | 41150 | 0.0074 |
| 0.8151 | 41200 | 0.0072 |
| 0.8161 | 41250 | 0.0076 |
| 0.8171 | 41300 | 0.0073 |
| 0.8181 | 41350 | 0.0075 |
| 0.8191 | 41400 | 0.0076 |
| 0.8201 | 41450 | 0.0074 |
| 0.8211 | 41500 | 0.0076 |
| 0.8220 | 41550 | 0.0073 |
| 0.8230 | 41600 | 0.0074 |
| 0.8240 | 41650 | 0.0072 |
| 0.8250 | 41700 | 0.0074 |
| 0.8260 | 41750 | 0.0074 |
| 0.8270 | 41800 | 0.0074 |
| 0.8280 | 41850 | 0.0075 |
| 0.8290 | 41900 | 0.0074 |
| 0.8300 | 41950 | 0.0075 |
| 0.8309 | 42000 | 0.0076 |
| 0.8319 | 42050 | 0.0078 |
| 0.8329 | 42100 | 0.0074 |
| 0.8339 | 42150 | 0.0077 |
| 0.8349 | 42200 | 0.0073 |
| 0.8359 | 42250 | 0.0076 |
| 0.8369 | 42300 | 0.0077 |
| 0.8379 | 42350 | 0.0077 |
| 0.8389 | 42400 | 0.0071 |
| 0.8398 | 42450 | 0.0076 |
| 0.8408 | 42500 | 0.0071 |
| 0.8418 | 42550 | 0.0075 |
| 0.8428 | 42600 | 0.0073 |
| 0.8438 | 42650 | 0.0073 |
| 0.8448 | 42700 | 0.0073 |
| 0.8458 | 42750 | 0.0068 |
| 0.8468 | 42800 | 0.0072 |
| 0.8478 | 42850 | 0.007 |
| 0.8487 | 42900 | 0.0073 |
| 0.8497 | 42950 | 0.0074 |
| 0.8507 | 43000 | 0.0073 |
| 0.8517 | 43050 | 0.0075 |
| 0.8527 | 43100 | 0.0073 |
| 0.8537 | 43150 | 0.0071 |
| 0.8547 | 43200 | 0.0069 |
| 0.8557 | 43250 | 0.0076 |
| 0.8567 | 43300 | 0.0076 |
| 0.8577 | 43350 | 0.0075 |
| 0.8586 | 43400 | 0.0076 |
| 0.8596 | 43450 | 0.0072 |
| 0.8606 | 43500 | 0.0072 |
| 0.8616 | 43550 | 0.0073 |
| 0.8626 | 43600 | 0.007 |
| 0.8636 | 43650 | 0.007 |
| 0.8646 | 43700 | 0.007 |
| 0.8656 | 43750 | 0.0075 |
| 0.8666 | 43800 | 0.0073 |
| 0.8675 | 43850 | 0.0071 |
| 0.8685 | 43900 | 0.0076 |
| 0.8695 | 43950 | 0.0073 |
| 0.8705 | 44000 | 0.0072 |
| 0.8715 | 44050 | 0.0076 |
| 0.8725 | 44100 | 0.0071 |
| 0.8735 | 44150 | 0.0072 |
| 0.8745 | 44200 | 0.0073 |
| 0.8755 | 44250 | 0.0071 |
| 0.8764 | 44300 | 0.0071 |
| 0.8774 | 44350 | 0.007 |
| 0.8784 | 44400 | 0.0074 |
| 0.8794 | 44450 | 0.0073 |
| 0.8804 | 44500 | 0.0071 |
| 0.8814 | 44550 | 0.0072 |
| 0.8824 | 44600 | 0.0069 |
| 0.8834 | 44650 | 0.0072 |
| 0.8844 | 44700 | 0.0073 |
| 0.8853 | 44750 | 0.0073 |
| 0.8863 | 44800 | 0.007 |
| 0.8873 | 44850 | 0.0073 |
| 0.8883 | 44900 | 0.0071 |
| 0.8893 | 44950 | 0.0069 |
| 0.8903 | 45000 | 0.0069 |
| 0.8913 | 45050 | 0.0072 |
| 0.8923 | 45100 | 0.0076 |
| 0.8933 | 45150 | 0.0073 |
| 0.8943 | 45200 | 0.0075 |
| 0.8952 | 45250 | 0.0074 |
| 0.8962 | 45300 | 0.007 |
| 0.8972 | 45350 | 0.0071 |
| 0.8982 | 45400 | 0.0069 |
| 0.8992 | 45450 | 0.007 |
| 0.9002 | 45500 | 0.0071 |
| 0.9012 | 45550 | 0.0072 |
| 0.9022 | 45600 | 0.0074 |
| 0.9032 | 45650 | 0.0071 |
| 0.9041 | 45700 | 0.0073 |
| 0.9051 | 45750 | 0.007 |
| 0.9061 | 45800 | 0.0069 |
| 0.9071 | 45850 | 0.0067 |
| 0.9081 | 45900 | 0.007 |
| 0.9091 | 45950 | 0.0071 |
| 0.9101 | 46000 | 0.0073 |
| 0.9111 | 46050 | 0.0073 |
| 0.9121 | 46100 | 0.0071 |
| 0.9130 | 46150 | 0.0072 |
| 0.9140 | 46200 | 0.007 |
| 0.9150 | 46250 | 0.0073 |
| 0.9160 | 46300 | 0.0072 |
| 0.9170 | 46350 | 0.0072 |
| 0.9180 | 46400 | 0.0074 |
| 0.9190 | 46450 | 0.0071 |
| 0.9200 | 46500 | 0.007 |
| 0.9210 | 46550 | 0.0073 |
| 0.9220 | 46600 | 0.0069 |
| 0.9229 | 46650 | 0.0071 |
| 0.9239 | 46700 | 0.0073 |
| 0.9249 | 46750 | 0.0069 |
| 0.9259 | 46800 | 0.0072 |
| 0.9269 | 46850 | 0.0068 |
| 0.9279 | 46900 | 0.0072 |
| 0.9289 | 46950 | 0.0069 |
| 0.9299 | 47000 | 0.0068 |
| 0.9309 | 47050 | 0.007 |
| 0.9318 | 47100 | 0.007 |
| 0.9328 | 47150 | 0.0071 |
| 0.9338 | 47200 | 0.0071 |
| 0.9348 | 47250 | 0.0071 |
| 0.9358 | 47300 | 0.007 |
| 0.9368 | 47350 | 0.0072 |
| 0.9378 | 47400 | 0.0073 |
| 0.9388 | 47450 | 0.0071 |
| 0.9398 | 47500 | 0.007 |
| 0.9407 | 47550 | 0.0072 |
| 0.9417 | 47600 | 0.0072 |
| 0.9427 | 47650 | 0.0068 |
| 0.9437 | 47700 | 0.007 |
| 0.9447 | 47750 | 0.0072 |
| 0.9457 | 47800 | 0.007 |
| 0.9467 | 47850 | 0.0069 |
| 0.9477 | 47900 | 0.0069 |
| 0.9487 | 47950 | 0.007 |
| 0.9496 | 48000 | 0.0069 |
| 0.9506 | 48050 | 0.0069 |
| 0.9516 | 48100 | 0.0071 |
| 0.9526 | 48150 | 0.0072 |
| 0.9536 | 48200 | 0.0071 |
| 0.9546 | 48250 | 0.0073 |
| 0.9556 | 48300 | 0.0068 |
| 0.9566 | 48350 | 0.007 |
| 0.9576 | 48400 | 0.0073 |
| 0.9586 | 48450 | 0.007 |
| 0.9595 | 48500 | 0.0072 |
| 0.9605 | 48550 | 0.007 |
| 0.9615 | 48600 | 0.0069 |
| 0.9625 | 48650 | 0.0068 |
| 0.9635 | 48700 | 0.007 |
| 0.9645 | 48750 | 0.0066 |
| 0.9655 | 48800 | 0.0068 |
| 0.9665 | 48850 | 0.0072 |
| 0.9675 | 48900 | 0.007 |
| 0.9684 | 48950 | 0.0072 |
| 0.9694 | 49000 | 0.007 |
| 0.9704 | 49050 | 0.0064 |
| 0.9714 | 49100 | 0.007 |
| 0.9724 | 49150 | 0.007 |
| 0.9734 | 49200 | 0.0072 |
| 0.9744 | 49250 | 0.0069 |
| 0.9754 | 49300 | 0.007 |
| 0.9764 | 49350 | 0.0068 |
| 0.9773 | 49400 | 0.0068 |
| 0.9783 | 49450 | 0.0066 |
| 0.9793 | 49500 | 0.0069 |
| 0.9803 | 49550 | 0.0074 |
| 0.9813 | 49600 | 0.007 |
| 0.9823 | 49650 | 0.0071 |
| 0.9833 | 49700 | 0.0068 |
| 0.9843 | 49750 | 0.007 |
| 0.9853 | 49800 | 0.0071 |
| 0.9862 | 49850 | 0.0071 |
| 0.9872 | 49900 | 0.0069 |
| 0.9882 | 49950 | 0.0069 |
| 0.9892 | 50000 | 0.0068 |
| 0.9902 | 50050 | 0.0072 |
| 0.9912 | 50100 | 0.0069 |
| 0.9922 | 50150 | 0.0067 |
| 0.9932 | 50200 | 0.0072 |
| 0.9942 | 50250 | 0.0068 |
| 0.9952 | 50300 | 0.0069 |
| 0.9961 | 50350 | 0.0074 |
| 0.9971 | 50400 | 0.0068 |
| 0.9981 | 50450 | 0.0072 |
| 0.9991 | 50500 | 0.007 |
</details>
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 5.3.0
- PyLate: 1.5.0
- Transformers: 4.48.3
- PyTorch: 2.4.0+cu124
- Accelerate: 1.13.0
- Datasets: 2.21.0
- Tokenizers: 0.21.4
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@inproceedings{DBLP:conf/cikm/ChaffinS25,
author = {Antoine Chaffin and
Rapha{"{e}}l Sourty},
editor = {Meeyoung Cha and
Chanyoung Park and
Noseong Park and
Carl Yang and
Senjuti Basu Roy and
Jessie Li and
Jaap Kamps and
Kijung Shin and
Bryan Hooi and
Lifang He},
title = {PyLate: Flexible Training and Retrieval for Late Interaction Models},
booktitle = {Proceedings of the 34th {ACM} International Conference on Information
and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
10-14, 2025},
pages = {6334--6339},
publisher = {{ACM}},
year = {2025},
url = {https://github.com/lightonai/pylate},
doi = {10.1145/3746252.3761608},
}
```
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