---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ner_column_bert-base-NER
  results: []
language:
- en
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---

<!-- 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. -->

# ner_column_bert-base-NER

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1872
- Precision: 0.7623
- Recall: 0.7753
- F1: 0.7688
- Accuracy: 0.9023

## 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: 64
- eval_batch_size: 64
- 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   | 702   | 0.6427          | 0.3025    | 0.2180 | 0.2534 | 0.7415   |
| 0.9329        | 2.0   | 1404  | 0.4771          | 0.4343    | 0.3587 | 0.3929 | 0.7955   |
| 0.546         | 3.0   | 2106  | 0.3983          | 0.5157    | 0.4530 | 0.4823 | 0.8242   |
| 0.546         | 4.0   | 2808  | 0.3748          | 0.5089    | 0.4758 | 0.4918 | 0.8305   |
| 0.4339        | 5.0   | 3510  | 0.2947          | 0.6362    | 0.6146 | 0.6252 | 0.8656   |
| 0.3658        | 6.0   | 4212  | 0.2818          | 0.6421    | 0.6231 | 0.6325 | 0.8664   |
| 0.3658        | 7.0   | 4914  | 0.2459          | 0.7108    | 0.6983 | 0.7045 | 0.8834   |
| 0.3221        | 8.0   | 5616  | 0.2665          | 0.6586    | 0.6404 | 0.6494 | 0.8701   |
| 0.2914        | 9.0   | 6318  | 0.2449          | 0.6880    | 0.6768 | 0.6823 | 0.8793   |
| 0.2657        | 10.0  | 7020  | 0.2411          | 0.7014    | 0.6862 | 0.6937 | 0.8824   |
| 0.2657        | 11.0  | 7722  | 0.2179          | 0.7261    | 0.7228 | 0.7244 | 0.8902   |
| 0.2453        | 12.0  | 8424  | 0.2301          | 0.6922    | 0.6919 | 0.6920 | 0.8858   |
| 0.2295        | 13.0  | 9126  | 0.2352          | 0.6768    | 0.6836 | 0.6802 | 0.8832   |
| 0.2295        | 14.0  | 9828  | 0.2020          | 0.7545    | 0.7499 | 0.7522 | 0.8970   |
| 0.2155        | 15.0  | 10530 | 0.2012          | 0.7449    | 0.7508 | 0.7478 | 0.8974   |
| 0.2064        | 16.0  | 11232 | 0.2036          | 0.7282    | 0.7402 | 0.7341 | 0.8960   |
| 0.2064        | 17.0  | 11934 | 0.1976          | 0.7390    | 0.7496 | 0.7443 | 0.8974   |
| 0.1978        | 18.0  | 12636 | 0.1859          | 0.7688    | 0.7828 | 0.7757 | 0.9040   |
| 0.1895        | 19.0  | 13338 | 0.1917          | 0.7574    | 0.7691 | 0.7632 | 0.9014   |
| 0.186         | 20.0  | 14040 | 0.1872          | 0.7623    | 0.7753 | 0.7688 | 0.9023   |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1+cu116
- Datasets 2.13.2
- Tokenizers 0.13.3