# DeBERTa-v3-base-mnli-fever-anli

## Model description

This model was trained on the MultiNLI dataset, which consists of 392 702 NLI hypothesis-premise pairs. The base model is DeBERTa-v3-base from Microsoft. 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. For a more powerful model, check out DeBERTa-v3-base-mnli-fever-anli which was trained on even more data.

## Intended uses & limitations

#### How to use the model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "MoritzLaurer/DeBERTa-v3-base-mnli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
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()
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)


### Training data

This model was trained on the MultiNLI dataset, which consists of 392 702 NLI hypothesis-premise pairs.

### Training procedure

DeBERTa-v3-base-mnli was trained using the Hugging Face trainer with the following hyperparameters.

training_args = TrainingArguments(
num_train_epochs=5,              # 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 matched test set and achieves 0.90 accuracy.

## Limitations and bias

Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases.

### BibTeX entry and citation info

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.

### Ideas for cooperation or questions?

If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or LinkedIn

### Debugging and issues

Note that DeBERTa-v3 was released recently and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers==4.13 might solve some issues.