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---
language:
- en
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
task_categories:
- feature-extraction
- sentence-similarity
pretty_name: AllNLI
tags:
- sentence-transformers
dataset_info:
- config_name: pair-class-distill
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': contradiction
'1': entailment
'2': neutral
---
# Dataset Card for AllNLI
This dataset is a concatenation of the [SNLI](https://huggingface.co/datasets/stanfordnlp/snli) and [MultiNLI](https://huggingface.co/datasets/nyu-mll/multi_nli) datasets.
This is the same dataset as `sentence-transformers/all-nli` `pair-class` split;
however, the label ids are not identical, and teacher scores have been added from `dleemiller/ModernCE-large-nli`.
I have also added hashes for score lookup, since a lookup must be added into a custom loss function,
if using the sentence transformers CrossEncoder trainer.
The hashes were computed straightforwardly as follows:
```
df.hash = df.apply(lambda x: hashlib.md5(f"{x.premise}\n{x.hypothesis}".encode()).hexdigest(), axis=1)
```
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