---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- fin
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fin2
  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: 0.9362745098039216
    - name: Recall
      type: recall
      value: 0.7609561752988048
    - name: F1
      type: f1
      value: 0.8395604395604396
    - name: Accuracy
      type: accuracy
      value: 0.9742916119346969
---

<!-- 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. -->

# fin2

This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the fin dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2405
- Precision: 0.9363
- Recall: 0.7610
- F1: 0.8396
- Accuracy: 0.9743

## 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  | 0.2186          | 0.7980    | 0.6454 | 0.7137 | 0.9653   |
| No log        | 2.0   | 258  | 0.2109          | 0.9487    | 0.7371 | 0.8296 | 0.9734   |
| No log        | 3.0   | 387  | 0.2531          | 0.9746    | 0.7649 | 0.8571 | 0.9743   |
| 0.1166        | 4.0   | 516  | 0.2345          | 0.9403    | 0.7530 | 0.8363 | 0.9741   |
| 0.1166        | 5.0   | 645  | 0.2405          | 0.9363    | 0.7610 | 0.8396 | 0.9743   |


### Framework versions

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