---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fin
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fin6
  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: 0.8237410071942446
    - name: Recall
      type: recall
      value: 0.9123505976095617
    - name: F1
      type: f1
      value: 0.8657844990548205
    - name: Accuracy
      type: accuracy
      value: 0.9836742353161944
---

<!-- 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. -->

# fin6

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: 0.0732
- Precision: 0.8237
- Recall: 0.9124
- F1: 0.8658
- Accuracy: 0.9837

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 129  | 0.0922          | 0.6559    | 0.8127 | 0.7260 | 0.9739   |
| No log        | 2.0   | 258  | 0.0471          | 0.8889    | 0.9243 | 0.9062 | 0.9910   |
| No log        | 3.0   | 387  | 0.0620          | 0.8419    | 0.9124 | 0.8757 | 0.9825   |
| 0.0622        | 4.0   | 516  | 0.0651          | 0.8156    | 0.9163 | 0.8630 | 0.9805   |
| 0.0622        | 5.0   | 645  | 0.0508          | 0.8614    | 0.9163 | 0.8880 | 0.9872   |
| 0.0622        | 6.0   | 774  | 0.0467          | 0.8988    | 0.9203 | 0.9094 | 0.9916   |
| 0.0622        | 7.0   | 903  | 0.0713          | 0.8099    | 0.9163 | 0.8598 | 0.9822   |
| 0.0052        | 8.0   | 1032 | 0.0767          | 0.8214    | 0.9163 | 0.8663 | 0.9824   |
| 0.0052        | 9.0   | 1161 | 0.0739          | 0.8179    | 0.9124 | 0.8625 | 0.9831   |
| 0.0052        | 10.0  | 1290 | 0.0732          | 0.8237    | 0.9124 | 0.8658 | 0.9837   |


### Framework versions

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