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# roberta-large-ner-new

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1106
- Precision: 0.9670
- Recall: 0.9604
- F1: 0.9637
- Accuracy: 0.9600

## 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: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1241        | 0.71  | 5000  | 0.1161          | 0.9618    | 0.9505 | 0.9561 | 0.9521   |
| 0.0993        | 1.42  | 10000 | 0.1132          | 0.9633    | 0.9568 | 0.9600 | 0.9562   |
| 0.0812        | 2.13  | 15000 | 0.1223          | 0.9662    | 0.9574 | 0.9618 | 0.9580   |
| 0.074         | 2.84  | 20000 | 0.1118          | 0.9661    | 0.9607 | 0.9634 | 0.9598   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "roberta-large", "model-index": [{"name": "roberta-large-ner-new", "results": []}]}
transformers safetensors roberta token-classification generated_from_trainer base_model:roberta-large license:mit autotrain_compatible endpoints_compatible region:us