Update README.md

README.md

@@ -7,31 +7,34 @@ tags:
 metrics:
 - accuracy
 widget:
-- text: "70-85% of the population needs to get vaccinated against the novel coronavirus to achieve herd immunity."
+- text: "I first thought that I liked the movie, but upon second thought it was actually disappointing. [SEP] The movie was good."
 
 ---
 # 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). 
+The base model is [DeBERTa-v3-base from Microsoft](https://huggingface.co/microsoft/deberta-v3-base). The v3 variant of DeBERTa 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"])
+
+premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing."
+hypothesis = "The movie was good."
+
+input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
+output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
 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.
 
@@ -48,7 +51,6 @@ training_args = TrainingArguments(
     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. The metric used is accuracy.
 
@@ -56,16 +58,8 @@ mnli-m | mnli-mm | fever-nli | anli-all | anli-r3
 ---------|----------|---------|----------|----------
 0.903 | 0.903 | 0.777 | 0.579 | 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}
-}
-```
+If you want to cite this model, please cite the original DeBERTa paper, the respective NLI datasets and include a link to this model on the Hugging Face hub.