---
license: mit
base_model: dslim/bert-base-NER
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Products_NER8
  results: []
---

<!-- 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. -->

# Products_NER8

This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2028
- Precision: 0.9227
- Recall: 0.9267
- F1: 0.9247
- Accuracy: 0.9446

## 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1326        | 1.0   | 1235  | 0.1052          | 0.8887    | 0.9121 | 0.9003 | 0.9386   |
| 0.0959        | 2.0   | 2470  | 0.0927          | 0.8742    | 0.9085 | 0.8910 | 0.9417   |
| 0.0824        | 3.0   | 3705  | 0.0931          | 0.8970    | 0.9174 | 0.9070 | 0.9433   |
| 0.079         | 4.0   | 4940  | 0.0948          | 0.9067    | 0.9209 | 0.9137 | 0.9432   |
| 0.0762        | 5.0   | 6175  | 0.0962          | 0.8963    | 0.9179 | 0.9070 | 0.9437   |
| 0.0721        | 6.0   | 7410  | 0.1030          | 0.9095    | 0.9223 | 0.9159 | 0.9443   |
| 0.0683        | 7.0   | 8645  | 0.1070          | 0.9128    | 0.9233 | 0.9181 | 0.9439   |
| 0.0637        | 8.0   | 9880  | 0.1178          | 0.9157    | 0.9240 | 0.9199 | 0.9439   |
| 0.059         | 9.0   | 11115 | 0.1215          | 0.9176    | 0.9248 | 0.9212 | 0.9443   |
| 0.0527        | 10.0  | 12350 | 0.1367          | 0.9189    | 0.9247 | 0.9218 | 0.9438   |
| 0.0475        | 11.0  | 13585 | 0.1504          | 0.9199    | 0.9250 | 0.9224 | 0.9441   |
| 0.0431        | 12.0  | 14820 | 0.1484          | 0.9207    | 0.9259 | 0.9233 | 0.9446   |
| 0.0389        | 13.0  | 16055 | 0.1706          | 0.9224    | 0.9267 | 0.9246 | 0.9446   |
| 0.0368        | 14.0  | 17290 | 0.1847          | 0.9223    | 0.9265 | 0.9244 | 0.9445   |
| 0.0351        | 15.0  | 18525 | 0.2028          | 0.9227    | 0.9267 | 0.9247 | 0.9446   |


### Framework versions

- Transformers 4.33.0
- Pytorch 1.13.1+cu117
- Datasets 2.1.0
- Tokenizers 0.13.3