# RoBERTa NLI (Natural Language Inference) 
This model is a fine-tuned model of [roberta-large](https://huggingface.co/roberta-large) after being trained on a **mixture of NLI datasets**.

This model can classify a pair of sentence (a <u>premise</u> and a <u>claim</u>) into 3 classes:
- 'entailment': the claim can logically be inferred from the premise
- 'contradiction': the claim contradicts the premise
- 'neutral': the premise is unrelated or do not provide sufficient information to validate the claim

This model can also be used for **zero-shot classification tasks** !
Please take a look at this [repo](https://github.com/AntoineBlanot/zero-nlp) for more information on zero-shot classification tasks.

# Usage
This model has been trained in an efficient way and thus cannot be load directly from HuggingFace's hub. To use that model, please follow instructions on this [repo](https://github.com/AntoineBlanot/efficient-llm).

For **zero-shot classification** tasks, please take a look at this [repo](https://github.com/AntoineBlanot/zero-nlp).

# Data used for training
- multi_nli
- snli
- scitail

# Evaluation results

| Data | Accuracy |
|:---:|:---------:|
| MNLI (val. m) | 0.894 |
| MNLI (val. mm) | 0.895 |
| SNLI (val.) | 0.920 |
| SciTail (val.) | 0.934 |
{"language": ["en"], "datasets": ["multi_nli", "snli", "scitail"], "metrics": ["accuracy", "f1"], "pipeline_tag": "zero-shot-classification"}
transformers pytorch roberta feature-extraction zero-shot-classification en dataset:multi_nli dataset:snli dataset:scitail endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #feature-extraction #zero-shot-classification #en #dataset-multi_nli #dataset-snli #dataset-scitail #endpoints_compatible #region-us 

RoBERTa NLI (Natural Language Inference)
========================================


This model is a fine-tuned model of roberta-large after being trained on a mixture of NLI datasets.


This model can classify a pair of sentence (a premise and a claim) into 3 classes:


* 'entailment': the claim can logically be inferred from the premise
* 'contradiction': the claim contradicts the premise
* 'neutral': the premise is unrelated or do not provide sufficient information to validate the claim


This model can also be used for zero-shot classification tasks !
Please take a look at this repo for more information on zero-shot classification tasks.


Usage
=====


This model has been trained in an efficient way and thus cannot be load directly from HuggingFace's hub. To use that model, please follow instructions on this repo.


For zero-shot classification tasks, please take a look at this repo.


Data used for training
======================


* multi\_nli
* snli
* scitail


Evaluation results
==================




TAGS
#transformers #pytorch #roberta #feature-extraction #zero-shot-classification #en #dataset-multi_nli #dataset-snli #dataset-scitail #endpoints_compatible #region-us 
