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--- |
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language: |
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- en |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 1M<n<10M |
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task_categories: |
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- feature-extraction |
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- sentence-similarity |
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pretty_name: NLI for SimCSE |
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tags: |
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- sentence-transformers |
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dataset_info: |
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- config_name: triplet |
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features: |
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- name: anchor |
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dtype: string |
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- name: positive |
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dtype: string |
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- name: negative |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 51033641 |
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num_examples: 274951 |
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download_size: 33517191 |
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dataset_size: 51033641 |
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- config_name: triplet-7 |
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features: |
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- name: anchor |
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dtype: string |
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- name: positive |
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dtype: string |
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- name: negative_1 |
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dtype: string |
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- name: negative_2 |
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dtype: string |
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- name: negative_3 |
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dtype: string |
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- name: negative_4 |
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dtype: string |
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- name: negative_5 |
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dtype: string |
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- name: negative_6 |
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dtype: string |
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- name: negative_7 |
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dtype: string |
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splits: |
|
- name: train |
|
num_bytes: 129065964 |
|
num_examples: 273540 |
|
download_size: 87886620 |
|
dataset_size: 129065964 |
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- config_name: triplet-all |
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features: |
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- name: anchor |
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dtype: string |
|
- name: positive |
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dtype: string |
|
- name: negative |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 357145333 |
|
num_examples: 1925996 |
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download_size: 94616052 |
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dataset_size: 357145333 |
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configs: |
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- config_name: triplet |
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data_files: |
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- split: train |
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path: triplet/train-* |
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- config_name: triplet-7 |
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data_files: |
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- split: train |
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path: triplet-7/train-* |
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- config_name: triplet-all |
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data_files: |
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- split: train |
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path: triplet-all/train-* |
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--- |
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# Dataset Card for NLI for SimCSE |
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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). |
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Despite being labeled as Natural Language Inference (NLI), this dataset can be used for training/finetuning an embedding model for semantic textual similarity. |
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## Dataset Subsets |
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### `triplet` subset |
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|
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* Columns: "anchor", "positive", "negative" |
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* Column types: `str`, `str`, `str` |
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* Examples: |
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```python |
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{ |
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'anchor': 'One of our number will carry out your instructions minutely.', |
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'positive': 'A member of my team will execute your orders with immense precision.', |
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'negative': 'We have no one free at the moment so you have to take action yourself.' |
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} |
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``` |
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* 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. |
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* Deduplified: No |
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### `triplet-7` subset |
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* Columns: "anchor", "positive", "negative_1", "negative_2", "negative_3", "negative_4", "negative_5", "negative_6", "negative_7" |
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* Column types: `str`, `str`, `str`, `str`, `str`, `str`, `str` |
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* Examples: |
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```python |
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{ |
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'anchor': 'One of our number will carry out your instructions minutely.', |
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'positive': 'A member of my team will execute your orders with immense precision.', |
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'negative_1': 'We have no one free at the moment so you have to take action yourself.', |
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'negative_2': 'A poodle is running through the grass.', |
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'negative_3': 'Investment and planning are growing industries in Jamaica.', |
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'negative_4': 'A bearded man is rocking out on an acoustic guitar', |
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'negative_5': 'The people are sunbathing on the beach.', |
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'negative_6': 'A construction worker installs a door.', |
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'negative_7': 'A crowd has gathered because of a dangerous situation.' |
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} |
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``` |
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* 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). |
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* Deduplified: No |
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### `triplet-all` subset |
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* Columns: "anchor", "positive", "negative" |
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* Column types: `str`, `str`, `str` |
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* Examples: |
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```python |
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{ |
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'anchor': 'One of our number will carry out your instructions minutely.', |
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'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.' |
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} |
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``` |
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* 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 each negative, but making a separate sample with each of the negatives. |
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* Deduplified: No |