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# en-multinerd-ner-unmasked-upsampled

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: 0.5953
- Precision: 0.8771
- Recall: 0.7115
- F1: 0.7857
- Accuracy: 0.9634
- Per-precision: 0.9944
- Per-recall: 0.9959
- Per-f1: 0.9951
- Org-precision: 0.9328
- Org-recall: 0.9434
- Org-f1: 0.9381
- Loc-precision: 0.9697
- Loc-recall: 0.9736
- Loc-f1: 0.9717
- Dis-precision: 0.0
- Dis-recall: 0.0
- Dis-f1: 0.0
- Anim-precision: 0.6852
- Anim-recall: 0.7293
- Anim-f1: 0.7065

## 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 | 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|
| 0.0262        | 1.0   | 8801  | 0.4482          | 0.8697    | 0.7072 | 0.7801 | 0.9628   | 0.9932        | 0.9947     | 0.9939 | 0.9305        | 0.9291     | 0.9298 | 0.9620        | 0.9731     | 0.9675 | 0.0           | 0.0        | 0.0    | 0.6740         | 0.6764      | 0.6752  |
| 0.0143        | 2.0   | 17602 | 0.5709          | 0.8714    | 0.7112 | 0.7832 | 0.9630   | 0.9961        | 0.9947     | 0.9954 | 0.9282        | 0.9393     | 0.9337 | 0.9680        | 0.9710     | 0.9695 | 0.0           | 0.0        | 0.0    | 0.6631         | 0.7584      | 0.7075  |
| 0.0084        | 3.0   | 26403 | 0.5953          | 0.8771    | 0.7115 | 0.7857 | 0.9634   | 0.9944        | 0.9959     | 0.9951 | 0.9328        | 0.9434     | 0.9381 | 0.9697        | 0.9736     | 0.9717 | 0.0           | 0.0        | 0.0    | 0.6852         | 0.7293      | 0.7065  |


### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- 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-unmasked-upsampled", "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