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# en-multinerd-masked-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: 0.0251
- Precision: 0.9353
- Recall: 0.9420
- F1: 0.9386
- Accuracy: 0.9917
- Per-precision: 0.9945
- Per-recall: 0.9953
- Per-f1: 0.9949
- Org-precision: 0.9261
- Org-recall: 0.9434
- Org-f1: 0.9347
- Loc-precision: 0.9679
- Loc-recall: 0.9718
- Loc-f1: 0.9699
- Dis-precision: 0.7003
- Dis-recall: 0.7156
- Dis-f1: 0.7079
- Anim-precision: 0.7021
- Anim-recall: 0.7169
- Anim-f1: 0.7094

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|
| 0.0235        | 1.0   | 8205  | 0.0268          | 0.9171    | 0.9466 | 0.9316 | 0.9907   | 0.9943        | 0.9960     | 0.9951 | 0.8943        | 0.9521     | 0.9223 | 0.9595        | 0.9713     | 0.9654 | 0.6531        | 0.7298     | 0.6893 | 0.6518         | 0.7593      | 0.7014  |
| 0.0151        | 2.0   | 16410 | 0.0251          | 0.9353    | 0.9420 | 0.9386 | 0.9917   | 0.9945        | 0.9953     | 0.9949 | 0.9261        | 0.9434     | 0.9347 | 0.9679        | 0.9718     | 0.9699 | 0.7003        | 0.7156     | 0.7079 | 0.7021         | 0.7169      | 0.7094  |


### 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-masked-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