Update README.md

README.md

@@ -6,8 +6,17 @@ license: mit
 We provide the train, dev, and test sets. 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:
@@ -16,13 +25,26 @@ To access MLe-SNLI, you can use the HuggingFace Datasets API to load the dataset
 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
+    })
+})
 """
 ```