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# trained_model

This model is a fine-tuned version of [Dizex/FoodBaseBERT-NER](https://huggingface.co/Dizex/FoodBaseBERT-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2967
- Precision: 0.6784
- Recall: 0.7160
- F1: 0.6967
- Accuracy: 0.9368

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 209  | 0.1880          | 0.6032    | 0.6975 | 0.6469 | 0.9269   |
| No log        | 2.0   | 418  | 0.1874          | 0.6275    | 0.7140 | 0.6679 | 0.9344   |
| 0.2256        | 3.0   | 627  | 0.1885          | 0.6522    | 0.7099 | 0.6798 | 0.9372   |
| 0.2256        | 4.0   | 836  | 0.2022          | 0.6704    | 0.7366 | 0.7020 | 0.9408   |
| 0.1043        | 5.0   | 1045 | 0.2527          | 0.6473    | 0.7099 | 0.6771 | 0.9392   |
| 0.1043        | 6.0   | 1254 | 0.2578          | 0.6699    | 0.7140 | 0.6912 | 0.9392   |
| 0.1043        | 7.0   | 1463 | 0.2784          | 0.6628    | 0.7078 | 0.6846 | 0.9382   |
| 0.0557        | 8.0   | 1672 | 0.2967          | 0.6784    | 0.7160 | 0.6967 | 0.9368   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "Dizex/FoodBaseBERT-NER", "model-index": [{"name": "trained_model", "results": []}]}
transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:Dizex/FoodBaseBERT-NER license:mit autotrain_compatible endpoints_compatible region:us