Create README.md

README.md

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+---
+datasets:
+- multi_nli
+- snli
+- scitail
+language:
+- en
+metrics:
+- accuracy
+- f1
+pipeline_tag: zero-shot-classification
+---
+# 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 |