metadata

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

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