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DeBERTa-v3-base-mnli-fever-anli

Zero-Shot Classification
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deberta-v2
text-classification
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DeBERTa-v3-base-mnli-fever-anli / README.md
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metadata
language:
  - en
license: mit
tags:
  - text-classification
  - zero-shot-classification
datasets:
  - multi_nli
  - anli
  - fever
metrics:
  - accuracy
pipeline_tag: zero-shot-classification
model-index:
  - name: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
    results:
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: anli
          type: anli
          config: plain_text
          split: test_r3
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      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: anli
          type: anli
          config: plain_text
          split: test_r1
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            name: Accuracy
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          name: Natural Language Inference
        dataset:
          name: multi_nli
          type: multi_nli
          config: default
          split: validation_mismatched
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      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: anli
          type: anli
          config: plain_text
          split: dev_r1
        metrics:
          - type: accuracy
            value: 0.737
            name: Accuracy
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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. 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 highest performance (but less speed), I recommend using https://huggingface.co/MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli.

How to use the model

Simple zero-shot classification pipeline 

from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli")
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)

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-anli"
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)

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. The metric used is accuracy.

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.

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

Debugging and issues

Note that 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.

Model Recycling

Evaluation on 36 datasets using MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli as a base model yields average score of 79.69 in comparison to 79.04 by microsoft/deberta-v3-base.

The model is ranked 2nd among all tested models for the microsoft/deberta-v3-base architecture as of 09/01/2023.

Results:

20_newsgroup ag_news amazon_reviews_multi anli boolq cb cola copa dbpedia esnli financial_phrasebank imdb isear mnli mrpc multirc poem_sentiment qnli qqp rotten_tomatoes rte sst2 sst_5bins stsb trec_coarse trec_fine tweet_ev_emoji tweet_ev_emotion tweet_ev_hate tweet_ev_irony tweet_ev_offensive tweet_ev_sentiment wic wnli wsc yahoo_answers
85.8072 90.4333 67.32 59.625 85.107 91.0714 85.8102 67 79.0333 91.6327 82.5 94.02 71.6428 89.5749 89.7059 64.1708 88.4615 93.575 91.4148 89.6811 86.2816 94.6101 57.0588 91.5508 97.6 91.2 45.264 82.6179 54.5455 74.3622 84.8837 71.6949 71.0031 69.0141 68.2692 71.3333

For more information, see: Model Recycling