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# en-multinerd-ner

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0490
- Precision: 0.8904
- Recall: 0.8960
- F1: 0.8932
- Accuracy: 0.9829
- Per-precision: 0.9943
- Per-recall: 0.9964
- Per-f1: 0.9953
- Org-precision: 0.9347
- Org-recall: 0.9463
- Org-f1: 0.9405
- Loc-precision: 0.9663
- Loc-recall: 0.9721
- Loc-f1: 0.9692
- Dis-precision: 0.6965
- Dis-recall: 0.7215
- Dis-f1: 0.7088
- Anim-precision: 0.6922
- Anim-recall: 0.7257
- Anim-f1: 0.7086

## 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: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy | Per-precision | Per-recall | Per-f1 | Org-precision | Org-recall | Org-f1 | Loc-precision | Loc-recall | Loc-f1 | Dis-precision | Dis-recall | Dis-f1 | Anim-precision | Anim-recall | Anim-f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|
| 0.0445        | 1.0   | 8205  | 0.0523          | 0.8584    | 0.8999 | 0.8786 | 0.9808   | 0.9932        | 0.9964     | 0.9948 | 0.8947        | 0.9529     | 0.9229 | 0.9595        | 0.9708     | 0.9651 | 0.6459        | 0.7392     | 0.6894 | 0.6534         | 0.7513      | 0.6989  |
| 0.0308        | 2.0   | 16410 | 0.0490          | 0.8904    | 0.8960 | 0.8932 | 0.9829   | 0.9943        | 0.9964     | 0.9953 | 0.9347        | 0.9463     | 0.9405 | 0.9663        | 0.9721     | 0.9692 | 0.6965        | 0.7215     | 0.7088 | 0.6922         | 0.7257      | 0.7086  |


### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "en-multinerd-ner", "results": []}]}
transformers tensorboard safetensors distilbert token-classification generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us