metadata

license: mit
base_model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli
tags:
  - generated_from_trainer
datasets:
  - sem_eval_2024_task_2
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: results2
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: sem_eval_2024_task_2
          type: sem_eval_2024_task_2
          config: sem_eval_2024_task_2_source
          split: validation
          args: sem_eval_2024_task_2_source
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.71
          - name: Precision
            type: precision
            value: 0.7228353140916808
          - name: Recall
            type: recall
            value: 0.71
          - name: F1
            type: f1
            value: 0.705762987012987

results2

This model is a fine-tuned version of MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli on the sem_eval_2024_task_2 dataset. It achieves the following results on the evaluation set:

Loss: 0.6038
Accuracy: 0.71
Precision: 0.7228
Recall: 0.71
F1: 0.7058

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.6794	1.0	107	0.6548	0.595	0.5978	0.595	0.5921
0.6734	2.0	214	0.6038	0.71	0.7228	0.71	0.7058

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0