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squeezebert-uncased-News_About_Gold

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metadata
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. 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)

Class Distribution:

Length of Input Text (in Words)

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