Update README.md

README.md

@@ -1,4 +1,16 @@
 ---
+language:
+- en
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+task_categories:
+- feature-extraction
+- sentence-similarity
+pretty_name: NLI for SimCSE
+tags:
+- sentence-transformers
 dataset_info:
 - config_name: triplet
   features:
@@ -68,3 +80,61 @@ configs:
   - split: train
     path: triplet-all/train-*
 ---
+
+# Dataset Card for NLI for SimCSE
+
+This is a reformatting of the NLI for SimCSE Dataset used to train the [BGE-M3 model](https://huggingface.co/BAAI/bge-m3). See the full BGE-M3 dataset in [Shitao/bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data).
+Despite being labeled as Natural Language Inference (NLI), this dataset can be used for training/finetuning an embedding model for semantic textual similarity.
+
+## Dataset Subsets
+
+### `triplet` subset
+
+* Columns: "anchor", "positive", "negative"
+* Column types: `str`, `str`, `str`
+* Examples:
+    ```python
+    {
+      'anchor': 'One of our number will carry out your instructions minutely.',
+      'positive': 'A member of my team will execute your orders with immense precision.',
+      'negative': 'We have no one free at the moment so you have to take action yourself.'
+    }
+    ```
+* Collection strategy: Reading the jsonl file in the `en_NLI_data` directory in [Shitao/bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) and taking only the first negative.
+* Deduplified: No
+
+### `triplet-7` subset
+
+* Columns: "anchor", "positive", "negative_1", "negative_2", "negative_3", "negative_4", "negative_5", "negative_6", "negative_7"
+* Column types: `str`, `str`, `str`, `str`, `str`, `str`, `str`
+* Examples:
+    ```python
+    {
+      'anchor': 'One of our number will carry out your instructions minutely.',
+      'positive': 'A member of my team will execute your orders with immense precision.',
+      'negative_1': 'We have no one free at the moment so you have to take action yourself.',
+      'negative_2': 'A poodle is running through the grass.',
+      'negative_3': 'Investment and planning are growing industries in Jamaica.',
+      'negative_4': 'A bearded man is rocking out on an acoustic guitar',
+      'negative_5': 'The people are sunbathing on the beach.',
+      'negative_6': 'A construction worker installs a door.',
+      'negative_7': 'A crowd has gathered because of a dangerous situation.'
+    }
+    ```
+* Collection strategy: Reading the jsonl file in the `en_NLI_data` directory in [Shitao/bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) and taking all samples that have 7 negatives (which is by far the majority).
+* Deduplified: No
+
+### `triplet-all` subset
+
+* Columns: "anchor", "positive", "negative"
+* Column types: `str`, `str`, `str`
+* Examples:
+    ```python
+    {
+      'anchor': 'One of our number will carry out your instructions minutely.',
+      'positive': 'A member of my team will execute your orders with immense precision.',
+      'negative': 'We have no one free at the moment so you have to take action yourself.'
+    }
+    ```
+* Collection strategy: Reading the jsonl file in the `en_NLI_data` directory in [Shitao/bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) and taking only each negative, but making a separate sample with each of the negatives.
+* Deduplified: No