---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Products_NER2
  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_NER2

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3482
- Precision: 0.9155
- Recall: 0.9208
- F1: 0.9182
- Accuracy: 0.9400

## 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: 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2267        | 1.0   | 2470  | 0.1614          | 0.8379    | 0.8791 | 0.8580 | 0.9212   |
| 0.1363        | 2.0   | 4940  | 0.1230          | 0.8602    | 0.8968 | 0.8781 | 0.9332   |
| 0.1047        | 3.0   | 7410  | 0.1183          | 0.8808    | 0.9063 | 0.8934 | 0.9360   |
| 0.0931        | 4.0   | 9880  | 0.1139          | 0.8909    | 0.9119 | 0.9013 | 0.9387   |
| 0.085         | 5.0   | 12350 | 0.1153          | 0.8889    | 0.9110 | 0.8998 | 0.9390   |
| 0.0835        | 6.0   | 14820 | 0.1257          | 0.9043    | 0.9165 | 0.9104 | 0.9398   |
| 0.0728        | 7.0   | 17290 | 0.1218          | 0.8987    | 0.9149 | 0.9067 | 0.9393   |
| 0.069         | 8.0   | 19760 | 0.1457          | 0.9040    | 0.9154 | 0.9097 | 0.9389   |
| 0.0616        | 9.0   | 22230 | 0.1606          | 0.9090    | 0.9166 | 0.9128 | 0.9386   |
| 0.0559        | 10.0  | 24700 | 0.1726          | 0.9122    | 0.9189 | 0.9156 | 0.9397   |
| 0.0504        | 11.0  | 27170 | 0.1998          | 0.9131    | 0.9192 | 0.9161 | 0.9396   |
| 0.043         | 12.0  | 29640 | 0.2015          | 0.9126    | 0.9194 | 0.9160 | 0.9402   |
| 0.0389        | 13.0  | 32110 | 0.2388          | 0.9129    | 0.9195 | 0.9162 | 0.9394   |
| 0.035         | 14.0  | 34580 | 0.2569          | 0.9135    | 0.9202 | 0.9169 | 0.9397   |
| 0.0311        | 15.0  | 37050 | 0.2718          | 0.9156    | 0.9207 | 0.9181 | 0.9400   |
| 0.028         | 16.0  | 39520 | 0.2886          | 0.9158    | 0.9208 | 0.9183 | 0.9403   |
| 0.0246        | 17.0  | 41990 | 0.3054          | 0.9145    | 0.9201 | 0.9173 | 0.9392   |
| 0.0212        | 18.0  | 44460 | 0.3252          | 0.9155    | 0.9206 | 0.9180 | 0.9398   |
| 0.0192        | 19.0  | 46930 | 0.3333          | 0.9157    | 0.9210 | 0.9183 | 0.9402   |
| 0.017         | 20.0  | 49400 | 0.3482          | 0.9155    | 0.9208 | 0.9182 | 0.9400   |


### Framework versions

- Transformers 4.33.2
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3