---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- fin
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fin5
  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: 0.9243027888446215
    - name: Recall
      type: recall
      value: 0.9243027888446215
    - name: F1
      type: f1
      value: 0.9243027888446215
    - name: Accuracy
      type: accuracy
      value: 0.9908666100254885
---

<!-- 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. -->

# fin5

This model is a fine-tuned version of [nlpaueb/sec-bert-shape](https://huggingface.co/nlpaueb/sec-bert-shape) on the fin dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0752
- Precision: 0.9243
- Recall: 0.9243
- F1: 0.9243
- Accuracy: 0.9909

## 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  | 0.0825          | 0.8327    | 0.8924 | 0.8615 | 0.9811   |
| No log        | 2.0   | 258  | 0.0633          | 0.8593    | 0.9243 | 0.8906 | 0.9866   |
| No log        | 3.0   | 387  | 0.0586          | 0.9038    | 0.9363 | 0.9198 | 0.9894   |
| 0.0547        | 4.0   | 516  | 0.0607          | 0.9357    | 0.9283 | 0.932  | 0.9911   |
| 0.0547        | 5.0   | 645  | 0.0656          | 0.9216    | 0.9363 | 0.9289 | 0.9904   |
| 0.0547        | 6.0   | 774  | 0.0692          | 0.9249    | 0.9323 | 0.9286 | 0.9909   |
| 0.0547        | 7.0   | 903  | 0.0716          | 0.9246    | 0.9283 | 0.9264 | 0.9904   |
| 0.0019        | 8.0   | 1032 | 0.0742          | 0.9213    | 0.9323 | 0.9267 | 0.9909   |
| 0.0019        | 9.0   | 1161 | 0.0748          | 0.9246    | 0.9283 | 0.9264 | 0.9909   |
| 0.0019        | 10.0  | 1290 | 0.0752          | 0.9243    | 0.9243 | 0.9243 | 0.9909   |


### Framework versions

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