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DunnBC22/bert-base-uncased-News_About_Gold

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

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bert-base-uncased-NewsAboutGold

This model is a fine-tuned version of bert-base-uncased. It achieves the following results on the evaluation set:

—Loss: 0.2995
—Accuracy: 0.9139
—Weighted f1: 0.9133
—Micro f1: 0.9139
—Macro f1: 0.8758
—Weighted recall: 0.9139
—Micro recall: 0.9139
—Macro recall: 0.8647
—Weighted precision: 0.9131
—Micro precision: 0.9139
—Macro precision: 0.8885

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-%20Bert-Base%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) /Images/Input%20Word%20Length.png)

Class Distribution:

Length of Input Text (in Words) /Images/Class%20Distribution.png)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

—learning_rate: 2e-05
—trainbatchsize: 64
—evalbatchsize: 64
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: 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
0.6809	1.0	133	0.3347	0.8974	0.8928	0.8974	0.8043	0.8974	0.8974	0.7710	0.8966	0.8974	0.8800
0.2778	2.0	266	0.2844	0.9144	0.9145	0.9144	0.8668	0.9144	0.9144	0.8689	0.9150	0.9144	0.8650
0.2022	3.0	399	0.2823	0.9172	0.9166	0.9172	0.8820	0.9172	0.9172	0.8691	0.9165	0.9172	0.8968
0.1496	4.0	532	0.2856	0.9167	0.9164	0.9167	0.8838	0.9167	0.9167	0.8686	0.9167	0.9167	0.9027
0.1196	5.0	665	0.2995	0.9139	0.9133	0.9139	0.8758	0.9139	0.9139	0.8647	0.9131	0.9139	0.8885

Framework versions

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

License Notice

This model is a fine-tuned derivative of a pretrained model. Users must comply with the original model license.

Dataset Notice

This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions.