---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fin
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fin1
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: fin
      type: fin
      config: default
      split: train
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.8315412186379928
    - name: Recall
      type: recall
      value: 0.9243027888446215
    - name: F1
      type: f1
      value: 0.8754716981132076
    - name: Accuracy
      type: accuracy
      value: 0.985175455057234
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# fin1

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the fin dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0778
- Precision: 0.8315
- Recall: 0.9243
- F1: 0.8755
- Accuracy: 0.9852

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 129  | 0.0860          | 0.8535    | 0.9283 | 0.8893 | 0.9904   |
| No log        | 2.0   | 258  | 0.1513          | 0.7993    | 0.9203 | 0.8556 | 0.9799   |
| No log        | 3.0   | 387  | 0.0977          | 0.8221    | 0.9203 | 0.8684 | 0.9831   |
| 0.0017        | 4.0   | 516  | 0.0783          | 0.8286    | 0.9243 | 0.8738 | 0.9848   |
| 0.0017        | 5.0   | 645  | 0.0778          | 0.8315    | 0.9243 | 0.8755 | 0.9852   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2