
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert-finetuned-ner13_woa

This model is a fine-tuned version of [Gladiator/microsoft-deberta-v3-large_ner_wnut_17](https://huggingface.co/Gladiator/microsoft-deberta-v3-large_ner_wnut_17) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4044
- Precision: 0.7599
- Recall: 0.5981
- F1: 0.6693
- Accuracy: 0.9542

## 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: 2e-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: cosine
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 425  | 0.2781          | 0.6504    | 0.5431 | 0.5919 | 0.9505   |
| 0.029         | 2.0   | 850  | 0.4056          | 0.7021    | 0.5610 | 0.6237 | 0.9495   |
| 0.0134        | 3.0   | 1275 | 0.3914          | 0.7458    | 0.5861 | 0.6564 | 0.9532   |
| 0.0095        | 4.0   | 1700 | 0.3982          | 0.7626    | 0.5993 | 0.6711 | 0.9544   |
| 0.0026        | 5.0   | 2125 | 0.4044          | 0.7599    | 0.5981 | 0.6693 | 0.9542   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "Gladiator/microsoft-deberta-v3-large_ner_wnut_17", "model-index": [{"name": "bert-finetuned-ner13_woa", "results": []}]}
transformers tensorboard safetensors deberta-v2 token-classification generated_from_trainer base_model:Gladiator/microsoft-deberta-v3-large_ner_wnut_17 license:mit autotrain_compatible endpoints_compatible region:us