transformers pytorch tensorboard safetensors bert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: gbert_extend
  results: []
---

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# gbert_extend

This model is a fine-tuned version of [deepset/gbert-base](https://huggingface.co/deepset/gbert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0333
- Precision: 0.8809
- Recall: 0.9274
- F1: 0.9036
- Accuracy: 0.9897

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0389        | 1.0   | 1500 | 0.0334          | 0.8621    | 0.9269 | 0.8933 | 0.9892   |
| 0.0228        | 2.0   | 3000 | 0.0333          | 0.8809    | 0.9274 | 0.9036 | 0.9897   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
