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# en-multinerd-ner-more-training

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: 0.0563
- Precision: 0.8942
- Recall: 0.9050
- F1: 0.8996
- Accuracy: 0.9836
- Per-precision: 0.9952
- Per-recall: 0.9965
- Per-f1: 0.9959
- Org-precision: 0.9412
- Org-recall: 0.9474
- Org-f1: 0.9443
- Loc-precision: 0.9691
- Loc-recall: 0.9752
- Loc-f1: 0.9721
- Dis-precision: 0.7157
- Dis-recall: 0.7440
- Dis-f1: 0.7295
- Anim-precision: 0.6870
- Anim-recall: 0.7240
- Anim-f1: 0.7050

## 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: 4

### 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|
| 0.0447        | 1.0   | 8205  | 0.0524          | 0.8583    | 0.9010 | 0.8791 | 0.9808   | 0.9939        | 0.9959     | 0.9949 | 0.8846        | 0.9547     | 0.9183 | 0.9626        | 0.9727     | 0.9676 | 0.6472        | 0.7457     | 0.6930 | 0.6607         | 0.7522      | 0.7035  |
| 0.0309        | 2.0   | 16410 | 0.0503          | 0.8800    | 0.9028 | 0.8913 | 0.9825   | 0.9940        | 0.9965     | 0.9953 | 0.9304        | 0.9474     | 0.9388 | 0.9640        | 0.9712     | 0.9676 | 0.7072        | 0.7369     | 0.7218 | 0.6765         | 0.7469      | 0.7100  |
| 0.0209        | 3.0   | 24615 | 0.0520          | 0.8908    | 0.9042 | 0.8975 | 0.9836   | 0.9953        | 0.9967     | 0.9960 | 0.9365        | 0.9478     | 0.9421 | 0.9680        | 0.9750     | 0.9715 | 0.7163        | 0.7268     | 0.7215 | 0.7013         | 0.7434      | 0.7217  |
| 0.0138        | 4.0   | 32820 | 0.0563          | 0.8942    | 0.9050 | 0.8996 | 0.9836   | 0.9952        | 0.9965     | 0.9959 | 0.9412        | 0.9474     | 0.9443 | 0.9691        | 0.9752     | 0.9721 | 0.7157        | 0.7440     | 0.7295 | 0.6870         | 0.7240      | 0.7050  |


### 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-more-training", "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