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# tst_fine-tuning-lilt

This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.2131
- eval_ANSWER: {'precision': 0.8539976825028969, 'recall': 0.9020807833537332, 'f1': 0.8773809523809523, 'number': 817}
- eval_HEADER: {'precision': 0.6666666666666666, 'recall': 0.47058823529411764, 'f1': 0.5517241379310345, 'number': 119}
- eval_QUESTION: {'precision': 0.8663239074550129, 'recall': 0.9387186629526463, 'f1': 0.9010695187165776, 'number': 1077}
- eval_overall_precision: 0.8534
- eval_overall_recall: 0.8962
- eval_overall_f1: 0.8742
- eval_overall_accuracy: 0.8048
- eval_runtime: 1.2663
- eval_samples_per_second: 39.484
- eval_steps_per_second: 5.528
- step: 0

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1

{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "SCUT-DLVCLab/lilt-roberta-en-base", "model-index": [{"name": "tst_fine-tuning-lilt", "results": []}]}
transformers tensorboard safetensors lilt token-classification generated_from_trainer base_model:SCUT-DLVCLab/lilt-roberta-en-base license:mit autotrain_compatible endpoints_compatible region:us