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# distilbert_system_A

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0547
- Precision: 0.8996
- Recall: 0.9132
- F1: 0.9063
- Accuracy: 0.9850

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0278        | 1.0   | 8205  | 0.0434          | 0.8992    | 0.8977 | 0.8984 | 0.9843   |
| 0.0161        | 2.0   | 16410 | 0.0477          | 0.9067    | 0.9065 | 0.9066 | 0.9851   |
| 0.0097        | 3.0   | 24615 | 0.0547          | 0.8996    | 0.9132 | 0.9063 | 0.9850   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["Babelscape/multinerd"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-cased", "model-index": [{"name": "distilbert_system_A", "results": []}]}
transformers tensorboard safetensors distilbert token-classification generated_from_trainer en dataset:Babelscape/multinerd base_model:distilbert-base-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us