---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test4
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: ner
      type: ner
      config: default
      split: train
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.594855305466238
    - name: Recall
      type: recall
      value: 0.6423611111111112
    - name: F1
      type: f1
      value: 0.6176961602671119
    - name: Accuracy
      type: accuracy
      value: 0.9579571605593911
---

<!-- 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. -->

# test4

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3100
- Precision: 0.5949
- Recall: 0.6424
- F1: 0.6177
- Accuracy: 0.9580

## 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: 1e-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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 418  | 0.2052          | 0.2415    | 0.2465 | 0.2440 | 0.9423   |
| 0.3341        | 2.0   | 836  | 0.1816          | 0.4286    | 0.4792 | 0.4525 | 0.9513   |
| 0.1296        | 3.0   | 1254 | 0.2039          | 0.4589    | 0.5035 | 0.4801 | 0.9526   |
| 0.0727        | 4.0   | 1672 | 0.2130          | 0.5237    | 0.5764 | 0.5488 | 0.9566   |
| 0.0553        | 5.0   | 2090 | 0.2290          | 0.5171    | 0.5764 | 0.5452 | 0.9551   |
| 0.0412        | 6.0   | 2508 | 0.2351          | 0.5390    | 0.5521 | 0.5455 | 0.9555   |
| 0.0412        | 7.0   | 2926 | 0.2431          | 0.5280    | 0.5903 | 0.5574 | 0.9542   |
| 0.0321        | 8.0   | 3344 | 0.2490          | 0.5825    | 0.625  | 0.6030 | 0.9570   |
| 0.0249        | 9.0   | 3762 | 0.2679          | 0.5764    | 0.5764 | 0.5764 | 0.9573   |
| 0.0192        | 10.0  | 4180 | 0.2574          | 0.5506    | 0.6042 | 0.5762 | 0.9558   |
| 0.0206        | 11.0  | 4598 | 0.2857          | 0.5498    | 0.5938 | 0.5710 | 0.9559   |
| 0.0147        | 12.0  | 5016 | 0.2638          | 0.5548    | 0.5972 | 0.5753 | 0.9550   |
| 0.0147        | 13.0  | 5434 | 0.2771          | 0.5677    | 0.5972 | 0.5821 | 0.9577   |
| 0.0129        | 14.0  | 5852 | 0.3016          | 0.5761    | 0.6181 | 0.5963 | 0.9549   |
| 0.0118        | 15.0  | 6270 | 0.3055          | 0.5587    | 0.6111 | 0.5837 | 0.9570   |
| 0.0099        | 16.0  | 6688 | 0.2937          | 0.5682    | 0.6076 | 0.5872 | 0.9564   |
| 0.0099        | 17.0  | 7106 | 0.3075          | 0.5313    | 0.6181 | 0.5714 | 0.9531   |
| 0.0085        | 18.0  | 7524 | 0.3079          | 0.6026    | 0.6424 | 0.6218 | 0.9580   |
| 0.0085        | 19.0  | 7942 | 0.3082          | 0.5833    | 0.6319 | 0.6067 | 0.9572   |
| 0.0074        | 20.0  | 8360 | 0.3100          | 0.5949    | 0.6424 | 0.6177 | 0.9580   |


### Framework versions

- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1