---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: harem
      type: harem
      config: default
      split: validation
      args: default
    metrics:
    - name: Precision
      type: precision
      value: 0.3251366120218579
    - name: Recall
      type: recall
      value: 0.34097421203438394
    - name: F1
      type: f1
      value: 0.3328671328671328
    - name: Accuracy
      type: accuracy
      value: 0.8684278684278685
---

<!-- 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. -->

# bert-base-cased-finetuned-ner

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5103
- Precision: 0.3251
- Recall: 0.3410
- F1: 0.3329
- Accuracy: 0.8684

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 4    | 1.1734          | 0.0       | 0.0    | 0.0    | 0.8083   |
| No log        | 2.0   | 8    | 0.9781          | 0.0       | 0.0    | 0.0    | 0.8086   |
| No log        | 3.0   | 12   | 0.8915          | 0.0       | 0.0    | 0.0    | 0.8086   |
| No log        | 4.0   | 16   | 0.7901          | 0.0       | 0.0    | 0.0    | 0.8086   |
| No log        | 5.0   | 20   | 0.7202          | 0.0       | 0.0    | 0.0    | 0.8086   |
| No log        | 6.0   | 24   | 0.6846          | 0.4286    | 0.0344 | 0.0637 | 0.8130   |
| No log        | 7.0   | 28   | 0.6596          | 0.2014    | 0.0802 | 0.1148 | 0.8306   |
| No log        | 8.0   | 32   | 0.6355          | 0.1615    | 0.0745 | 0.1020 | 0.8324   |
| No log        | 9.0   | 36   | 0.6193          | 0.1571    | 0.0946 | 0.1181 | 0.8345   |
| No log        | 10.0  | 40   | 0.6106          | 0.1295    | 0.1032 | 0.1148 | 0.8335   |
| No log        | 11.0  | 44   | 0.5919          | 0.1680    | 0.1232 | 0.1421 | 0.8350   |
| No log        | 12.0  | 48   | 0.5789          | 0.2051    | 0.1375 | 0.1647 | 0.8384   |
| No log        | 13.0  | 52   | 0.5827          | 0.1611    | 0.1375 | 0.1484 | 0.8355   |
| No log        | 14.0  | 56   | 0.5638          | 0.2281    | 0.1862 | 0.2050 | 0.8433   |
| No log        | 15.0  | 60   | 0.5576          | 0.1879    | 0.1691 | 0.1780 | 0.8420   |
| No log        | 16.0  | 64   | 0.5485          | 0.2110    | 0.1862 | 0.1979 | 0.8456   |
| No log        | 17.0  | 68   | 0.5479          | 0.2401    | 0.2264 | 0.2330 | 0.8500   |
| No log        | 18.0  | 72   | 0.5460          | 0.2406    | 0.2378 | 0.2392 | 0.8503   |
| No log        | 19.0  | 76   | 0.5374          | 0.2531    | 0.2350 | 0.2437 | 0.8542   |
| No log        | 20.0  | 80   | 0.5365          | 0.2364    | 0.2493 | 0.2427 | 0.8539   |
| No log        | 21.0  | 84   | 0.5284          | 0.2462    | 0.2350 | 0.2405 | 0.8552   |
| No log        | 22.0  | 88   | 0.5306          | 0.2812    | 0.2837 | 0.2825 | 0.8601   |
| No log        | 23.0  | 92   | 0.5262          | 0.2722    | 0.2722 | 0.2722 | 0.8573   |
| No log        | 24.0  | 96   | 0.5306          | 0.2447    | 0.2665 | 0.2551 | 0.8555   |
| No log        | 25.0  | 100  | 0.5249          | 0.2785    | 0.3009 | 0.2893 | 0.8594   |
| No log        | 26.0  | 104  | 0.5201          | 0.2801    | 0.2865 | 0.2833 | 0.8586   |
| No log        | 27.0  | 108  | 0.5213          | 0.2806    | 0.2894 | 0.2849 | 0.8604   |
| No log        | 28.0  | 112  | 0.5207          | 0.2732    | 0.2951 | 0.2837 | 0.8612   |
| No log        | 29.0  | 116  | 0.5144          | 0.3027    | 0.3209 | 0.3115 | 0.8630   |
| No log        | 30.0  | 120  | 0.5135          | 0.3073    | 0.3381 | 0.3220 | 0.8648   |
| No log        | 31.0  | 124  | 0.5147          | 0.2953    | 0.3266 | 0.3102 | 0.8651   |
| No log        | 32.0  | 128  | 0.5121          | 0.2937    | 0.3181 | 0.3054 | 0.8645   |
| No log        | 33.0  | 132  | 0.5092          | 0.3061    | 0.3324 | 0.3187 | 0.8645   |
| No log        | 34.0  | 136  | 0.5064          | 0.3342    | 0.3696 | 0.3510 | 0.8677   |
| No log        | 35.0  | 140  | 0.5056          | 0.3191    | 0.3438 | 0.3310 | 0.8674   |
| No log        | 36.0  | 144  | 0.5091          | 0.3023    | 0.3352 | 0.3179 | 0.8661   |
| No log        | 37.0  | 148  | 0.5104          | 0.3061    | 0.3324 | 0.3187 | 0.8658   |
| No log        | 38.0  | 152  | 0.5100          | 0.3152    | 0.3324 | 0.3236 | 0.8677   |
| No log        | 39.0  | 156  | 0.5102          | 0.3243    | 0.3410 | 0.3324 | 0.8684   |
| No log        | 40.0  | 160  | 0.5103          | 0.3251    | 0.3410 | 0.3329 | 0.8684   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3