| --- |
| license: mit |
| --- |
| # Multilingual e-SNLI (MLe-SNLI) |
|
|
| In this repo, we provide the training, validation, and testing sets for **M**ulti**l**ingual **e-SNLI** (MLe-SNLI). For more details, find our report [here](https://github.com/rish-16/cs4248-project/blob/main/CS4248_Group19_Final_Report.pdf). |
|
|
| ## Dataset details |
| MLe-SNLI contains 500K training (`train`) samples of premise-hypothesis pairs along with their associated label and explanation. We take 100K training samples from the original e-SNLI (Camburu et al., 2018) dataset and translate them into 4 other languages (Spanish, German, Dutch, and French). We do the same for all 9824 testing (`test`) and validation (`dev`) samples, giving us 49120 samples for both `test` and `dev` splits. |
|
|
| | Column | Description | |
| |-----------------|---------------------------------------------------------------------------------| |
| | `premise` | Natural language premise sentence | |
| | `hypothesis` | Natural language hypothesis sentence | |
| | `label` | From `entailment`, `contradiction`, or `neutral` | |
| | `explanation_1` | Natural language justification for `label` | |
| | `language` | From English (`en`), Spanish (`es`), German (`de`), Dutch (`nl`), French (`fr`) | |
|
|
| > **WARNING:** the translation quality of MLe-SNLI may be compromised for some natural language samples because of quality issues in the original e-SNLI dataset that were not addressed in our [work](https://github.com/rish-16/cs4248-project). Use it at your own discretion. |
|
|
| ## Download Instructions |
| To access MLe-SNLI, you can use the HuggingFace Datasets API to load the dataset: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| mle_snli = load_dataset("rish16/MLe-SNLI") # loads a DatasetDict object |
| |
| train_data = mle_snli['train'] # 500K samples (100K per lang) |
| dev_data = mle_snli['dev'] # 49120 samples (9824 per lang) |
| test_data = mle_snli['test'] # 49120 samples (9824 per lang) |
| |
| print (mle_snli) |
| """ |
| DatasetDict({ |
| train: Dataset({ |
| features: ['premise', 'hypothesis', 'label', 'explanation_1', 'language'], |
| num_rows: 500000 |
| }) |
| test: Dataset({ |
| features: ['premise', 'hypothesis', 'label', 'explanation_1', 'language'], |
| num_rows: 49120 |
| }) |
| validation: Dataset({ |
| features: ['premise', 'hypothesis', 'label', 'explanation_1', 'language'], |
| num_rows: 49210 |
| }) |
| }) |
| """ |
| ``` |
