bort-News_About_Gold

This model is a fine-tuned version of amazon/bort. It achieves the following results on the evaluation set:

• Loss: 0.3791
• Accuracy: 0.8770
• Weighted f1: 0.8743
• Micro f1: 0.8770
• Macro f1: 0.7791
• Weighted recall: 0.8770
• Micro recall: 0.8770
• Macro recall: 0.7539
• Weighted precision: 0.8778
• Micro precision: 0.8770
• Macro precision: 0.8463

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-%20BORT%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:

Class Distribution:

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
1.0437	1.0	133	0.8379	0.6954	0.6800	0.6954	0.5285	0.6954	0.6954	0.5326	0.6944	0.6954	0.5434
0.6297	2.0	266	0.4715	0.8340	0.8209	0.8340	0.6267	0.8340	0.8340	0.6368	0.8111	0.8340	0.6187
0.4216	3.0	399	0.3984	0.8661	0.8616	0.8661	0.7464	0.8661	0.8661	0.7231	0.8698	0.8661	0.8597
0.3339	4.0	532	0.3808	0.8765	0.8748	0.8765	0.7825	0.8765	0.8765	0.7628	0.8774	0.8765	0.8304
0.2869	5.0	665	0.3791	0.8770	0.8743	0.8770	0.7791	0.8770	0.8770	0.7539	0.8778	0.8770	0.8463

Framework versions

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

License

This model is a fine-tuned version of Amazon's Bort model (Apache 2.0).

It was trained on the "Sentiment Analysis in Commodity Market (Gold)" dataset, which is licensed under CC BY-NC-ND 4.0.

This dataset prohibits commercial use and the creation of derivative works. As a result, this model may be subject to the same restrictions.

Users must review and comply with the dataset license before using this model.

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.