README.md

---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: squeezebert-uncased-News_About_Gold
  results: []
language:
- en
pipeline_tag: text-classification
---

# squeezebert-uncased-News_About_Gold

This model is a fine-tuned version of [squeezebert/squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased).
It achieves the following results on the evaluation set:
- Loss: 0.2643
- Accuracy: 0.9167
- F1
  - Weighted: 0.9166
  - Micro: 0.9167
  - Macro: 0.8749
- Recall
  - Weighted: 0.9167
  - Micro: 0.9167
  - Macro: 0.8684
- Precision
  - Weighted: 0.9168
  - Micro: 0.9167
  - Macro: 0.8822

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/News%20About%20Gold%20-%20Sentiment%20Analysis%20-%20SqueezeBERT%20with%20W%26B.ipynb

This project is part of a comparison of seven (7) transformers. Here is the README page for the comparison: https://github.com/DunnBC22/NLP_Projects/tree/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-in-commodity-market-gold

_Input Word Length:_

![Length of Input Text (in Words)](https://github.com/DunnBC22/NLP_Projects/raw/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/Images/Input%20Word%20Length.png)

_Class Distribution:_

![Length of Input Text (in Words)](https://github.com/DunnBC22/NLP_Projects/raw/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/Images/Class%20Distribution.png)

## 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