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# en-multinerd-ner-roberta

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0280
- Precision: 0.9421
- Recall: 0.9446
- F1: 0.9434
- Accuracy: 0.9903
- Per-precision: 0.9917
- Per-recall: 0.9970
- Per-f1: 0.9943
- Org-precision: 0.9766
- Org-recall: 0.9837
- Org-f1: 0.9801
- Loc-precision: 0.9959
- Loc-recall: 0.9934
- Loc-f1: 0.9947
- Dis-precision: 0.7665
- Dis-recall: 0.7655
- Dis-f1: 0.7660
- Anim-precision: 0.6897
- Anim-recall: 0.7039
- Anim-f1: 0.6967

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:--------------:|:-----------:|:-------:|
| 0.038         | 1.0   | 8205  | 0.0452          | 0.8742    | 0.9240 | 0.8984 | 0.9830   | 0.9963        | 0.9955     | 0.9959 | 0.9616        | 0.9795     | 0.9705 | 0.9894        | 0.9944     | 0.9919 | 0.6658        | 0.7487     | 0.7048 | 0.6415         | 0.7857      | 0.7063  |
| 0.0294        | 2.0   | 16410 | 0.0410          | 0.9154    | 0.9085 | 0.9119 | 0.9856   | 0.9952        | 0.9980     | 0.9966 | 0.9697        | 0.9814     | 0.9755 | 0.9929        | 0.9946     | 0.9938 | 0.7358        | 0.7162     | 0.7259 | 0.7107         | 0.6455      | 0.6765  |
| 0.0202        | 3.0   | 24615 | 0.0429          | 0.9023    | 0.9255 | 0.9137 | 0.9854   | 0.9950        | 0.9973     | 0.9961 | 0.9791        | 0.9766     | 0.9779 | 0.9905        | 0.9958     | 0.9931 | 0.7190        | 0.7622     | 0.7400 | 0.7247         | 0.7522      | 0.7382  |
| 0.0149        | 4.0   | 32820 | 0.0455          | 0.9122    | 0.9205 | 0.9163 | 0.9858   | 0.9955        | 0.9971     | 0.9963 | 0.9795        | 0.9781     | 0.9788 | 0.9928        | 0.9954     | 0.9941 | 0.7295        | 0.7605     | 0.7447 | 0.7174         | 0.7363      | 0.7267  |


### Framework versions

- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

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