
# 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:_

![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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 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
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "recall", "precision"], "pipeline_tag": "text-classification", "model-index": [{"name": "medium-base-News_About_Gold", "results": []}]}
transformers pytorch tensorboard funnel text-classification generated_from_trainer en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #funnel #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

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: 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 #funnel #text-classification #generated_from_trainer #en #license-apache-2.0 #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