Deberta V3 Base Mnli

🚀 DeBERTa-v3-base-mnli-fever-anli

This model is designed for zero - shot text classification. It was trained on the MultiNLI dataset and can be used to determine the logical relationship between a premise and a hypothesis.

🚀 Quick Start

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()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)

✨ Features

• Trained on MultiNLI: The model was trained on the MultiNLI dataset, which consists of 392,702 NLI hypothesis - premise pairs.
• Based on DeBERTa - v3: The v3 variant of DeBERTa substantially outperforms previous versions by including a different pre - training objective.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)

📚 Documentation

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](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. For a more powerful model, check out [DeBERTa - v3 - base - mnli - fever - anli](https://huggingface.co/MoritzLaurer/DeBERTa - v3 - base - mnli - fever - anli) which was trained on even more data.

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.

🔧 Technical Details

The model is based on the DeBERTa - v3 architecture. The v3 variant includes a different pre - training objective, which makes it perform better than previous versions. It was trained on the MultiNLI dataset, and the training used specific hyperparameters as shown in the training procedure section.

📄 License

No license information is provided in the original document.

⚠️ Important Note

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

💡 Usage Tip

If you have questions or ideas for cooperation, contact the author at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz - laurer/).

Model Recycling

[Evaluation on 36 datasets](https://ibm.github.io/model - recycling/model_gain_chart?avg=0.97&mnli_lp=nan&20_newsgroup=-0.39&ag_news=0.19&amazon_reviews_multi=0.10&anli=1.31&boolq=0.81&cb=8.93&cola=0.01&copa=13.60&dbpedia=-0.23&esnli=-0.51&financial_phrasebank=0.61&imdb=-0.26&isear=-0.35&mnli=-0.34&mrpc=1.24&multirc=1.50&poem_sentiment=-0.19&qnli=0.30&qqp=0.13&rotten_tomatoes=-0.55&rte=3.57&sst2=0.35&sst_5bins=0.39&stsb=1.10&trec_coarse=-0.36&trec_fine=-0.02&tweet_ev_emoji=1.11&tweet_ev_emotion=-0.35&tweet_ev_hate=1.43&tweet_ev_irony=-2.65&tweet_ev_offensive=-1.69&tweet_ev_sentiment=-1.51&wic=0.57&wnli=-2.61&wsc=9.95&yahoo_answers=-0.33&model_name=MoritzLaurer%2FDeBERTa - v3 - base - mnli&base_name=microsoft%2Fdeberta - v3 - base) using MoritzLaurer/DeBERTa - v3 - base - mnli as a base model yields an average score of 80.01 in comparison to 79.04 by microsoft/deberta - v3 - base.

The model is ranked 1st among all tested models for the microsoft/deberta - v3 - base architecture as of 09/01/2023.

Results:

For more information, see: [Model Recycling](https://ibm.github.io/model - recycling/)