metadata

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

mpnet-base-News_About_Gold

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

Loss: 0.3098
Accuracy: 0.9068
Weighted f1: 0.9068
Micro f1: 0.9068
Macro f1: 0.8351
Weighted recall: 0.9068
Micro recall: 0.9068
Macro recall: 0.8406
Weighted precision: 0.9071
Micro precision: 0.9068
Macro precision: 0.8309

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-%20MPNet-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:

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
0.8316	1.0	133	0.5146	0.8742	0.8604	0.8742	0.6541	0.8742	0.8742	0.6583	0.8487	0.8742	0.6515
0.4675	2.0	266	0.3833	0.8898	0.8857	0.8898	0.7813	0.8898	0.8898	0.7542	0.8862	0.8898	0.8298
0.3276	3.0	399	0.3464	0.8997	0.8985	0.8997	0.8302	0.8997	0.8997	0.8212	0.8984	0.8997	0.8408
0.2767	4.0	532	0.3098	0.9101	0.9103	0.9101	0.8412	0.9101	0.9101	0.8462	0.9106	0.9101	0.8367
0.2429	5.0	665	0.3098	0.9068	0.9068	0.9068	0.8351	0.9068	0.9068	0.8406	0.9071	0.9068	0.8309

Framework versions

Transformers 4.28.1
Pytorch 2.0.0
Datasets 2.11.0
Tokenizers 0.13.3