
# bort-News_About_Gold

This model is a fine-tuned version of [amazon/bort](https://huggingface.co/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:_

![Length of Input Text (in Words)](https://github.com/DunnBC22/NLP_Projects/raw/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/Images/Input%20Word%20Length.png)

_Class Distribution:_

![Length of Input Text (in Words)](https://github.com/DunnBC22/NLP_Projects/raw/main/Sentiment%20Analysis/Sentiment%20Analysis%20of%20Commodity%20News%20-%20Gold%20(Transformer%20Comparison)/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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 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
{"language": ["en"], "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall", "precision"], "pipeline_tag": "text-classification", "model-index": [{"name": "bort-News_About_Gold", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #autotrain_compatible #endpoints_compatible #region-us 

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: URL


This project is part of a comparison of seven (7) transformers. Here is the README page for the comparison: URL


Intended uses & limitations
---------------------------


This model is intended to demonstrate my ability to solve a complex problem using technology.


Training and evaluation data
----------------------------


Dataset Source: URL


*Input Word Length:*


!Length of Input Text (in Words)/Images/Input%20Word%URL)


*Class Distribution:*


!Length of Input Text (in Words)/Images/Class%URL)


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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #en #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


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