Create README.md

README.md

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+---
+language: 
+- en
+tags:
+- text-classification
+- zero-shot-classification
+metrics:
+- accuracy
+widget:
+- text: "70-85% of the population needs to get vaccinated against the novel coronavirus to achieve herd immunity."
+
+---
+# DeBERTa-v3-base-mnli-fever-anli
+## Model description
+This model was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs. This base model outperforms almost all large models on the [ANLI benchmark](https://github.com/facebookresearch/anli). 
+The base model is [DeBERTa-v3-base from Microsoft](https://huggingface.co/microsoft/deberta-v3-base). The v3 variant substantially outperforms previous versions of the model by including a different pre-training objective, see annex 11 of the original [DeBERTa paper](https://arxiv.org/pdf/2006.03654.pdf). 
+
+## Intended uses & limitations
+#### How to use the model
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model_name = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+text = "The new variant first detected in southern England in September is blamed for sharp rises in levels of positive tests in recent weeks in London, south-east England and the east of England"
+input = tokenizer(text, truncation=True, return_tensors="pt")
+output = model(input["input_ids"])
+prediction = torch.softmax(output["logits"][0], -1).tolist()
+label_names = ["entailment", "neutral", "contradiction"]
+prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
+print(prediction)
+```
+
+### Training data
+DeBERTa-v3-base-mnli-fever-anli was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs.
+
+### Training procedure
+DeBERTa-v3-base-mnli-fever-anli was trained using the Hugging Face trainer with the following hyperparameters.
+```
+training_args = TrainingArguments(
+    num_train_epochs=3,              # total number of training epochs
+    learning_rate=2e-05,
+    per_device_train_batch_size=32,   # batch size per device during training
+    per_device_eval_batch_size=32,    # batch size for evaluation
+    warmup_ratio=0.1,                # number of warmup steps for learning rate scheduler
+    weight_decay=0.06,               # strength of weight decay
+    fp16=True                        # mixed precision training
+)
+```
+
+### Eval results
+The model was evaluated using the test sets for MultiNLI and ANLI and the dev set for Fever-NLI
+dataset   | accuracy
+-------|---------
+mnli_m/mm  | 0.903/0.903
+fever-nli  | 0.777
+anli-all  | 0.579
+anli-r3   | 0.495
+
+## Limitations and bias
+Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. 
+
+### BibTeX entry and citation info
+```bibtex
+@unpublished{
+  title={DeBERTa-v3-base-mnli-fever-anli},
+  author={Moritz Laurer},
+  year={2021},
+  note={Unpublished paper}
+}
+```