metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: medium-base-News_About_Gold
    results: []
language:
  - en
pipeline_tag: text-classification

medium-base-News_About_Gold

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

Loss: 0.2838
Accuracy: 0.9172
Weighted f1: 0.9170
Micro f1: 0.9172
Macro f1: 0.8854
Weighted recall: 0.9172
Micro recall: 0.9172
Macro recall: 0.8859
Weighted precision: 0.9171
Micro precision: 0.9172
Macro precision: 0.8853

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-%20Funnel%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
0.7426	1.0	133	0.3820	0.8803	0.8636	0.8803	0.6690	0.8803	0.8803	0.6809	0.8862	0.8803	0.8992
0.332	2.0	266	0.3083	0.9007	0.8987	0.9007	0.8525	0.9007	0.9007	0.8402	0.9015	0.9007	0.8705
0.2381	3.0	399	0.2870	0.9106	0.9097	0.9106	0.8686	0.9106	0.9106	0.8539	0.9096	0.9106	0.8862
0.1911	4.0	532	0.2797	0.9163	0.9158	0.9163	0.8843	0.9163	0.9163	0.8819	0.9159	0.9163	0.8873
0.1584	5.0	665	0.2838	0.9172	0.9170	0.9172	0.8854	0.9172	0.9172	0.8859	0.9171	0.9172	0.8853

Framework versions

Transformers 4.28.1
Pytorch 2.0.0
Datasets 2.11.0
Tokenizers 0.13.3