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# tst_lilt_cord_xlm_ft

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: 0.2024
- Precision: 0.9578
- Recall: 0.9555
- F1: 0.9567
- Accuracy: 0.9657

## 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
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 0.25  | 100  | 0.4066          | 0.8691    | 0.8544 | 0.8617 | 0.8774   |
| No log        | 0.5   | 200  | 0.3936          | 0.8724    | 0.8633 | 0.8678 | 0.8820   |
| No log        | 0.75  | 300  | 0.4283          | 0.8802    | 0.8794 | 0.8798 | 0.8902   |
| No log        | 1.0   | 400  | 0.2634          | 0.9326    | 0.9183 | 0.9254 | 0.9419   |
| 0.3486        | 1.25  | 500  | 0.2464          | 0.9143    | 0.9150 | 0.9147 | 0.9401   |
| 0.3486        | 1.5   | 600  | 0.2368          | 0.9296    | 0.9296 | 0.9296 | 0.9460   |
| 0.3486        | 1.75  | 700  | 0.2537          | 0.9434    | 0.9442 | 0.9438 | 0.9552   |
| 0.3486        | 2.0   | 800  | 0.2233          | 0.9504    | 0.9466 | 0.9485 | 0.9552   |
| 0.3486        | 2.25  | 900  | 0.2449          | 0.9482    | 0.9482 | 0.9482 | 0.9593   |
| 0.1869        | 2.5   | 1000 | 0.2214          | 0.9540    | 0.9555 | 0.9547 | 0.9611   |
| 0.1869        | 2.75  | 1100 | 0.2304          | 0.9352    | 0.9337 | 0.9344 | 0.9446   |
| 0.1869        | 3.0   | 1200 | 0.2748          | 0.9432    | 0.9401 | 0.9417 | 0.9520   |
| 0.1869        | 3.25  | 1300 | 0.2104          | 0.9460    | 0.9490 | 0.9475 | 0.9579   |
| 0.1869        | 3.5   | 1400 | 0.2379          | 0.9496    | 0.9458 | 0.9477 | 0.9597   |
| 0.1043        | 3.75  | 1500 | 0.2067          | 0.9466    | 0.9466 | 0.9466 | 0.9579   |
| 0.1043        | 4.0   | 1600 | 0.2025          | 0.9562    | 0.9547 | 0.9555 | 0.9634   |
| 0.1043        | 4.25  | 1700 | 0.2082          | 0.9514    | 0.9506 | 0.9510 | 0.9602   |
| 0.1043        | 4.5   | 1800 | 0.1942          | 0.9547    | 0.9555 | 0.9551 | 0.9666   |
| 0.1043        | 4.75  | 1900 | 0.2007          | 0.9532    | 0.9547 | 0.9539 | 0.9652   |
| 0.0574        | 5.0   | 2000 | 0.2024          | 0.9578    | 0.9555 | 0.9567 | 0.9657   |


### 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": "tst_lilt_cord_xlm_ft", "results": []}]}
transformers tensorboard safetensors lilt token-classification generated_from_trainer base_model:nielsr/lilt-xlm-roberta-base license:mit autotrain_compatible endpoints_compatible region:us