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# LiLT-SER-SIN-Only

This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1090
- Precision: 0.7273
- Recall: 0.7607
- F1: 0.7436
- Accuracy: 0.7917

## 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: 5000

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0317        | 12.5  | 250  | 1.4306          | 0.6616    | 0.7180 | 0.6887 | 0.7715   |
| 0.0391        | 25.0  | 500  | 1.3242          | 0.7093    | 0.7279 | 0.7184 | 0.8231   |
| 0.0018        | 37.5  | 750  | 1.6821          | 0.6581    | 0.7574 | 0.7043 | 0.7900   |
| 0.002         | 50.0  | 1000 | 1.8163          | 0.6568    | 0.7279 | 0.6905 | 0.7906   |
| 0.0001        | 62.5  | 1250 | 2.0038          | 0.6898    | 0.7508 | 0.7190 | 0.7956   |
| 0.0161        | 75.0  | 1500 | 1.9679          | 0.6657    | 0.7311 | 0.6969 | 0.7771   |
| 0.0           | 87.5  | 1750 | 1.9512          | 0.6561    | 0.7443 | 0.6974 | 0.7861   |
| 0.0041        | 100.0 | 2000 | 2.1426          | 0.6717    | 0.7246 | 0.6972 | 0.7861   |
| 0.0021        | 112.5 | 2250 | 2.0468          | 0.7259    | 0.7639 | 0.7444 | 0.7928   |
| 0.0003        | 125.0 | 2500 | 2.1781          | 0.6607    | 0.7279 | 0.6927 | 0.7765   |
| 0.0001        | 137.5 | 2750 | 2.1556          | 0.6627    | 0.7344 | 0.6967 | 0.7704   |
| 0.0001        | 150.0 | 3000 | 2.2475          | 0.6730    | 0.7016 | 0.6870 | 0.7591   |
| 0.0001        | 162.5 | 3250 | 2.3865          | 0.6698    | 0.7115 | 0.6900 | 0.7378   |
| 0.0001        | 175.0 | 3500 | 2.2571          | 0.6606    | 0.7148 | 0.6866 | 0.7720   |
| 0.0009        | 187.5 | 3750 | 2.2343          | 0.7350    | 0.7639 | 0.7492 | 0.7878   |
| 0.0001        | 200.0 | 4000 | 2.1724          | 0.7143    | 0.7377 | 0.7258 | 0.7866   |
| 0.0           | 212.5 | 4250 | 2.1738          | 0.7045    | 0.7115 | 0.7080 | 0.7771   |
| 0.0           | 225.0 | 4500 | 2.0815          | 0.7248    | 0.7770 | 0.7500 | 0.7945   |
| 0.0           | 237.5 | 4750 | 2.1074          | 0.7273    | 0.7607 | 0.7436 | 0.7923   |
| 0.0           | 250.0 | 5000 | 2.1090          | 0.7273    | 0.7607 | 0.7436 | 0.7917   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "nielsr/lilt-xlm-roberta-base", "model-index": [{"name": "LiLT-SER-SIN-Only", "results": []}]}
transformers safetensors lilt token-classification generated_from_trainer base_model:nielsr/lilt-xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us