---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: squeezebert-uncased-News_About_Gold
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# squeezebert-uncased-News_About_Gold

This model is a fine-tuned version of [squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2643
- Accuracy: 0.9167
- Weighted f1: 0.9166
- Micro f1: 0.9167
- Macro f1: 0.8749
- Weighted recall: 0.9167
- Micro recall: 0.9167
- Macro recall: 0.8684
- Weighted precision: 0.9168
- Micro precision: 0.9167
- Macro precision: 0.8822

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.8756        | 1.0   | 133  | 0.4529          | 0.8699   | 0.8557      | 0.8699   | 0.6560   | 0.8699          | 0.8699       | 0.6727       | 0.8437             | 0.8699          | 0.6414          |
| 0.4097        | 2.0   | 266  | 0.3196          | 0.9026   | 0.8982      | 0.9026   | 0.7826   | 0.9026          | 0.9026       | 0.7635       | 0.9059             | 0.9026          | 0.8743          |
| 0.3147        | 3.0   | 399  | 0.2824          | 0.9115   | 0.9111      | 0.9115   | 0.8470   | 0.9115          | 0.9115       | 0.8319       | 0.9138             | 0.9115          | 0.8751          |
| 0.2685        | 4.0   | 532  | 0.2649          | 0.9186   | 0.9187      | 0.9186   | 0.8681   | 0.9186          | 0.9186       | 0.8602       | 0.9203             | 0.9186          | 0.8797          |
| 0.2479        | 5.0   | 665  | 0.2643          | 0.9167   | 0.9166      | 0.9167   | 0.8749   | 0.9167          | 0.9167       | 0.8684       | 0.9168             | 0.9167          | 0.8822          |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3