transformers tensorboard safetensors bert token-classification generated_from_trainer base_model:bert-base-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: tun-en-ner
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# tun-en-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2051
- Precision: 0.7569
- Recall: 0.8176
- F1: 0.7861
- Accuracy: 0.9381

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2205        | 1.0   | 2078 | 0.2123          | 0.7143    | 0.8032 | 0.7561 | 0.9295   |
| 0.1718        | 2.0   | 4156 | 0.1922          | 0.7461    | 0.8174 | 0.7801 | 0.9383   |
| 0.1322        | 3.0   | 6234 | 0.2051          | 0.7569    | 0.8176 | 0.7861 | 0.9381   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
