README.md

metadata

language:
  - en
license: mit
tags:
  - text-classification
  - zero-shot-classification
metrics:
  - accuracy
datasets:
  - multi_nli
  - anli
  - fever
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
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.495
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.4984740618243923
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.495
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.4984357572868885
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.49461028192371476
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.495
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.495
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.4942810999491704
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.495
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.4944671868893595
            verified: true
          - name: loss
            type: loss
            value: 1.8788293600082397
            verified: true
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: anli
          type: anli
          config: plain_text
          split: test_r1
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.712
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.7134839439315348
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.712
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.7134676028447461
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.7119814425203647
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.712
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.712
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.7119226991285647
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.712
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.7119242267218338
            verified: true
          - name: loss
            type: loss
            value: 1.0105403661727905
            verified: true
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: multi_nli
          type: multi_nli
          config: default
          split: validation_mismatched
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.902766476810415
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.9023816542652491
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.902766476810415
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.9034597464719761
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.9024304801555488
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.902766476810415
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.902766476810415
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.9023086094638595
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.902766476810415
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.9030161011457231
            verified: true
          - name: loss
            type: loss
            value: 0.3283354640007019
            verified: true
      - task:
          type: natural-language-inference
          name: Natural Language Inference
        dataset:
          name: anli
          type: anli
          config: plain_text
          split: dev_r1
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.737
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.737681071614645
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.737
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.7376755842752241
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.7369675064285843
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.737
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.737
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.7366853496239583
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.737
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.7366990292378379
            verified: true
          - name: loss
            type: loss
            value: 0.9349392056465149
            verified: true

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.