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---
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](https://huggingface.co/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:_
/Images/Input%20Word%20Length.png)
_Class Distribution:_
/Images/Class%20Distribution.png)
## 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 |