---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- token_classification_v2
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: favs_token_classification_v2_uncased
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: token_classification_v2
      type: token_classification_v2
      config: default
      split: train
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.6598639455782312
    - name: Recall
      type: recall
      value: 0.782258064516129
    - name: F1
      type: f1
      value: 0.7158671586715867
    - name: Accuracy
      type: accuracy
      value: 0.8546511627906976
---

<!-- 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. -->

# favs_token_classification_v2_uncased

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the token_classification_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5006
- Precision: 0.6599
- Recall: 0.7823
- F1: 0.7159
- Accuracy: 0.8547

## 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: 1.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 2.2447        | 1.0   | 13   | 1.9089          | 0.2       | 0.0968 | 0.1304 | 0.3576   |
| 1.9589        | 2.0   | 26   | 1.5848          | 0.2734    | 0.2823 | 0.2778 | 0.4477   |
| 1.729         | 3.0   | 39   | 1.3636          | 0.3128    | 0.4516 | 0.3696 | 0.6134   |
| 1.4278        | 4.0   | 52   | 1.1854          | 0.4302    | 0.5968 | 0.5    | 0.7122   |
| 1.3046        | 5.0   | 65   | 1.0341          | 0.5183    | 0.6855 | 0.5903 | 0.7413   |
| 1.1599        | 6.0   | 78   | 0.9163          | 0.5188    | 0.6694 | 0.5845 | 0.75     |
| 0.9263        | 7.0   | 91   | 0.8235          | 0.5399    | 0.7097 | 0.6132 | 0.7645   |
| 0.8721        | 8.0   | 104  | 0.7627          | 0.5176    | 0.7097 | 0.5986 | 0.7733   |
| 0.7879        | 9.0   | 117  | 0.7070          | 0.5366    | 0.7097 | 0.6111 | 0.7849   |
| 0.6881        | 10.0  | 130  | 0.6575          | 0.5427    | 0.7177 | 0.6181 | 0.7936   |
| 0.6414        | 11.0  | 143  | 0.6076          | 0.5660    | 0.7258 | 0.6360 | 0.8110   |
| 0.6096        | 12.0  | 156  | 0.5804          | 0.6090    | 0.7661 | 0.6786 | 0.8285   |
| 0.5812        | 13.0  | 169  | 0.5661          | 0.6282    | 0.7903 | 0.7000 | 0.8343   |
| 0.5006        | 14.0  | 182  | 0.5503          | 0.6144    | 0.7581 | 0.6787 | 0.8285   |
| 0.5289        | 15.0  | 195  | 0.5366          | 0.6267    | 0.7581 | 0.6861 | 0.8372   |
| 0.4447        | 16.0  | 208  | 0.5222          | 0.6419    | 0.7661 | 0.6985 | 0.8459   |
| 0.435         | 17.0  | 221  | 0.5120          | 0.6599    | 0.7823 | 0.7159 | 0.8517   |
| 0.4454        | 18.0  | 234  | 0.5058          | 0.6667    | 0.7903 | 0.7232 | 0.8547   |
| 0.422         | 19.0  | 247  | 0.5013          | 0.6599    | 0.7823 | 0.7159 | 0.8547   |
| 0.4285        | 20.0  | 260  | 0.5006          | 0.6599    | 0.7823 | 0.7159 | 0.8547   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1