---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- fin
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fin4
  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: 0.9209486166007905
    - name: Recall
      type: recall
      value: 0.9282868525896414
    - name: F1
      type: f1
      value: 0.9246031746031745
    - name: Accuracy
      type: accuracy
      value: 0.9913080347678609
---

<!-- 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. -->

# fin4

This model is a fine-tuned version of [nlpaueb/sec-bert-num](https://huggingface.co/nlpaueb/sec-bert-num) on the fin dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0549
- Precision: 0.9209
- Recall: 0.9283
- F1: 0.9246
- Accuracy: 0.9913

## 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  | 0.1041          | 0.8242    | 0.8406 | 0.8323 | 0.9788   |
| No log        | 2.0   | 258  | 0.0511          | 0.9173    | 0.9283 | 0.9228 | 0.9902   |
| No log        | 3.0   | 387  | 0.0430          | 0.9102    | 0.9283 | 0.9191 | 0.9907   |
| 0.0598        | 4.0   | 516  | 0.0501          | 0.9368    | 0.9442 | 0.9405 | 0.9922   |
| 0.0598        | 5.0   | 645  | 0.0436          | 0.9325    | 0.9363 | 0.9344 | 0.9924   |
| 0.0598        | 6.0   | 774  | 0.0489          | 0.9433    | 0.9283 | 0.9357 | 0.9917   |
| 0.0598        | 7.0   | 903  | 0.0499          | 0.932     | 0.9283 | 0.9301 | 0.9919   |
| 0.0028        | 8.0   | 1032 | 0.0537          | 0.9209    | 0.9283 | 0.9246 | 0.9913   |
| 0.0028        | 9.0   | 1161 | 0.0540          | 0.9170    | 0.9243 | 0.9206 | 0.9911   |
| 0.0028        | 10.0  | 1290 | 0.0549          | 0.9209    | 0.9283 | 0.9246 | 0.9913   |


### Framework versions

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