---
tags:
- generated_from_keras_callback
- AVeriTec
model-index:
  - name: deberta-v3-large-AVeriTeC-nli
    results:
      - task:
          type: text-classification
        dataset:
          name: chenxwh/AVeriTeC
          type: chenxwh/AVeriTeC
        metrics:
          - name: dev macro F1 score
            type: macro F1 score
            value: 0.71
          - name: dev macro recall
            type: macro recall
            value: 0.73
          - name: dev macro precision
            type: macro precision
            value: 0.71
          - name: dev accuracy
            type: accuracy
            value: 0.82 
license: mit
language:
- en
library_name: transformers
pipeline_tag: text-classification
base_model: microsoft/deberta-v3-large
datasets:
- chenxwh/AVeriTeC

---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# deberta-v3-large-AVeriTeC-nli

This model was finetuned from microsoft/deberta-v3-large on an AVeriTec dataset.
It achieves the following results on the evaluation set:

## Intended uses & limitations

This model is intended for usage in a pipeline for open-domain fact-checking task.

## Training and evaluation data

See chenxwh/AVeriTeC

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: adamw_torch
- training_precision: float16
- learning_rate: 1e-5
- per_device_train_batch_size: 32
- num_train_epochs: 10
- weight_decay: 0.01
- load_best_model_at_end: True #early stopping!
- warmup_ratio: 0.06

### Training results



### Framework versions

- Transformers 4.43.0
- TensorFlow 2.17.0
- Datasets 2.20.0
- Tokenizers 0.19.1