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metadata
language:
  - en
tags:
  - text-classification
  - zero-shot-classification
license: mit
metrics:
  - accuracy
datasets:
  - multi_nli
  - anli
  - fever
  - lingnli
  - alisawuffles/WANLI
widget:
  - text: >-
      I first thought that I really liked the movie, but upon second thought it
      was actually disappointing. [SEP] The movie was good.
model-index:
  - name: DeBERTa-v3-large-mnli-fever-anli-ling-wanli
    results:
      - task:
          type: text-classification
          name: Natural Language Inference
        dataset:
          type: multi_nli
          name: MultiNLI-matched
          split: validation_matched
        metrics:
          - type: accuracy
            value: 0,912
            verified: false
      - task:
          type: text-classification
          name: Natural Language Inference
        dataset:
          type: multi_nli
          name: MultiNLI-mismatched
          split: validation_mismatched
        metrics:
          - type: accuracy
            value: 0,908
            verified: false
      - task:
          type: text-classification
          name: Natural Language Inference
        dataset:
          type: anli
          name: ANLI-all
          split: test_r1+test_r2+test_r3
        metrics:
          - type: accuracy
            value: 0,702
            verified: false
      - task:
          type: text-classification
          name: Natural Language Inference
        dataset:
          type: anli
          name: ANLI-r3
          split: test_r3
        metrics:
          - type: accuracy
            value: 0,64
            verified: false
      - task:
          type: text-classification
          name: Natural Language Inference
        dataset:
          type: alisawuffles/WANLI
          name: WANLI
          split: test
        metrics:
          - type: accuracy
            value: 0,77
            verified: false
      - task:
          type: text-classification
          name: Natural Language Inference
        dataset:
          type: lingnli
          name: LingNLI
          split: test
        metrics:
          - type: accuracy
            value: 0,87
            verified: false

DeBERTa-v3-large-mnli-fever-anli-ling-wanli

Model description

This model was fine-tuned on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. This model is the best NLI and zero-shot model on the Hugging Face Hub as of 06.06.22. It significantly outperforms all other large models on the ANLI benchmark.

The foundation model is DeBERTa-v3-large from Microsoft. Released on 06.12.21, DeBERTa-v3-large is currently the best large-sized foundation model for text classification. It combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the paper

Intended uses & limitations

How to use the model

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli"
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-large-mnli-fever-anli-ling-wanli was trained on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. Note that SNLI was explicitly excluded due to quality issues with the dataset. More data does not necessarily make for better NLI models.

Training procedure

DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained using the Hugging Face trainer with the following hyperparameters. Note that longer training with more epochs hurt performance in my tests (overfitting).

training_args = TrainingArguments(
    num_train_epochs=4,              # total number of training epochs
    learning_rate=5e-06,
    per_device_train_batch_size=16,   # batch size per device during training
    gradient_accumulation_steps=2,    # doubles the effective batch_size to 32, while decreasing memory requirements
    per_device_eval_batch_size=64,    # batch size for evaluation
    warmup_ratio=0.06,                # number of warmup steps for learning rate scheduler
    weight_decay=0.01,               # strength of weight decay
    fp16=True                        # mixed precision training
)

Eval results

The model was evaluated using the test sets for MultiNLI, ANLI, LingNLI, WANLI and the dev set for Fever-NLI. The metric used is accuracy. The model achieves state-of-the-art performance on each dataset. Surprisingly, it outperforms the previous state-of-the-art on ANLI (ALBERT-XXL) by 8,3%. I assume that this is because ANLI was created to fool masked language models like RoBERTa (or ALBERT), while DeBERTa-v3 uses a better pre-training objective (RTD), disentangled attention and I fine-tuned it on higher quality NLI data.

Datasets mnli_test_m mnli_test_mm anli_test anli_test_r3 ling_test wanli_test
Accuracy 0.912 0.908 0.702 0.64 0.87 0.77
Speed (text/sec, A100 GPU) 696.0 697.0 488.0 425.0 828.0 980.0

Limitations and bias

Please consult the original DeBERTa-v3 paper and literature on different NLI datasets for more information on the training data and potential biases. The model will reproduce statistical patterns in the training data.

BibTeX entry and citation info

If you want to cite this model, please cite my preprint on low-resource text classification and the original DeBERTa-v3 paper.

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.