Deberta V3 Base Mnli Fever Docnli Ling 2c

🚀 DeBERTa-v3-base-mnli-fever-docnli-ling-2c

This model is trained on 8 NLI datasets, leveraging the DeBERTa-v3-base architecture to perform text classification and zero - shot classification tasks effectively.

🚀 Quick Start

This model is designed for text classification and zero - shot classification. It uses the DeBERTa - v3 - base architecture and is trained on multiple NLI datasets to learn long - range reasoning.

✨ Features

• Trained on 1.279.665 hypothesis - premise pairs from 8 NLI datasets, including DocNLI with long texts for long - range reasoning.
• The base model is [DeBERTa - v3 - base from Microsoft](https://huggingface.co/microsoft/deberta - v3 - base), which substantially outperforms previous versions with a different pre - training objective.
• Can be used for simple zero - shot classification and NLI use - cases.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

Simple zero - shot classification pipeline

from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c")
sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
candidate_labels = ["politics", "economy", "entertainment", "environment"]
output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
print(output)

Advanced Usage

NLI use - case

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

model_name = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c"
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", "not_entailment"]
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 1.279.665 hypothesis - premise pairs from 8 NLI datasets: MultiNLI, [Fever - NLI](https://github.com/easonnie/combine - FEVER - NSMN/blob/master/other_resources/nli_fever.md), LingNLI and DocNLI (which includes ANLI, QNLI, DUC, CNN/DailyMail, Curation).

It is the only model in the model hub trained on 8 NLI datasets, including DocNLI with very long texts to learn long - range reasoning. Note that the model was trained on binary NLI to predict either "entailment" or "not - entailment". The DocNLI merges the classes "neural" and "contradiction" into "not - entailment" to enable the inclusion of the DocNLI dataset.

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 as well as the DeBERTa - V3 paper.

For highest performance (but less speed), I recommend using https://huggingface.co/MoritzLaurer/DeBERTa - v3 - large - mnli - fever - anli - ling - wanli.

Training data

This model was trained on 1.279.665 hypothesis - premise pairs from 8 NLI datasets: MultiNLI, [Fever - NLI](https://github.com/easonnie/combine - FEVER - NSMN/blob/master/other_resources/nli_fever.md), LingNLI and DocNLI (which includes ANLI, QNLI, DUC, CNN/DailyMail, Curation).

Training procedure

DeBERTa - v3 - small - mnli - fever - docnli - ling - 2c 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 binary test sets for MultiNLI and ANLI and the binary dev set for Fever - NLI (two classes instead of three). The metric used is accuracy.

🔧 Technical Details

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 as well as the DeBERTa - V3 paper.

📄 License

This project is licensed under the MIT license.

Limitations and bias

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

Citation

If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k.

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](https://www.linkedin.com/in/moritz - laurer/)

Debugging and issues

⚠️ Important Note

DeBERTa - v3 was released on 06.12.21 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.