
# fnet-large-Financial_Sentiment_Analysis_v3

This model is a fine-tuned version of [google/fnet-large](https://huggingface.co/google/fnet-large).
It achieves the following results on the evaluation set:
- Loss: 0.4741
- Accuracy: 0.8248
- F1
  - Weighted: 0.8194
  - Micro: 0.8248
  - Macro: 0.7369
- Recall
  - Weighted: 0.8248
  - Micro: 0.8248
  - Macro: 0.7269
- Precision
  - Weighted: 0.8163
  - Micro: 0.8248
  - Macro: 0.7515

## Model description

This is a sentiment analysis (text classification) model concerning comments about finances.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Sentiment%20Analysis/Financial%20Sentiment%20Analysis/Financial_Sentiment_Analysis_v3.ipynb

## Intended uses & limitations

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

## Training and evaluation data

Dataset Sources:
- https://www.kaggle.com/datasets/sbhatti/financial-sentiment-analysis
- https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-for-financial-news

## 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: 10

### 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.6757        | 1.0   | 134  | 0.5890          | 0.5855   | 0.4739      | 0.5855   | 0.3628   | 0.5855          | 0.5855       | 0.4298       | 0.5912             | 0.5855          | 0.5210          |
| 0.4815        | 2.0   | 268  | 0.3994          | 0.7827   | 0.7789      | 0.7827   | 0.7156   | 0.7827          | 0.7827       | 0.7039       | 0.7878             | 0.7827          | 0.7388          |
| 0.314         | 3.0   | 402  | 0.3560          | 0.7991   | 0.7977      | 0.7991   | 0.7368   | 0.7991          | 0.7991       | 0.7252       | 0.8101             | 0.7991          | 0.7612          |
| 0.235         | 4.0   | 536  | 0.3278          | 0.8201   | 0.8217      | 0.8201   | 0.7549   | 0.8201          | 0.8201       | 0.7509       | 0.8274             | 0.8201          | 0.7631          |
| 0.1986        | 5.0   | 670  | 0.3574          | 0.8618   | 0.8655      | 0.8618   | 0.8209   | 0.8618          | 0.8618       | 0.8401       | 0.8723             | 0.8618          | 0.8084          |
| 0.1605        | 6.0   | 804  | 0.3886          | 0.7995   | 0.7803      | 0.7995   | 0.6588   | 0.7995          | 0.7995       | 0.6469       | 0.7781             | 0.7995          | 0.6987          |
| 0.1436        | 7.0   | 938  | 0.4040          | 0.8230   | 0.8207      | 0.8230   | 0.7442   | 0.8230          | 0.8230       | 0.7336       | 0.8210             | 0.8230          | 0.7576          |
| 0.1373        | 8.0   | 1072 | 0.4517          | 0.8169   | 0.8076      | 0.8169   | 0.7123   | 0.8169          | 0.8169       | 0.7020       | 0.8030             | 0.8169          | 0.7323          |
| 0.1271        | 9.0   | 1206 | 0.4533          | 0.8070   | 0.7945      | 0.8070   | 0.6892   | 0.8070          | 0.8070       | 0.6768       | 0.7906             | 0.8070          | 0.7169          |
| 0.1199        | 10.0  | 1340 | 0.4741          | 0.8248   | 0.8194      | 0.8248   | 0.7369   | 0.8248          | 0.8248       | 0.7269       | 0.8163             | 0.8248          | 0.7515          |


### Framework versions

- Transformers 4.27.4
- 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": "fnet-large-Financial_Sentiment_Analysis_v3", "results": []}]}
transformers pytorch tensorboard fnet text-classification generated_from_trainer en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #fnet #text-classification #generated_from_trainer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

fnet-large-Financial\_Sentiment\_Analysis\_v3
=============================================


This model is a fine-tuned version of google/fnet-large.
It achieves the following results on the evaluation set:


* Loss: 0.4741
* Accuracy: 0.8248
* F1
	+ Weighted: 0.8194
	+ Micro: 0.8248
	+ Macro: 0.7369
* Recall
	+ Weighted: 0.8248
	+ Micro: 0.8248
	+ Macro: 0.7269
* Precision
	+ Weighted: 0.8163
	+ Micro: 0.8248
	+ Macro: 0.7515


Model description
-----------------


This is a sentiment analysis (text classification) model concerning comments about finances.


For more information on how it was created, check out the following link: URL


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


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


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


Dataset Sources:


* URL
* 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: 10


### Training results



### Framework versions


* Transformers 4.27.4
* 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: 10 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #fnet #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: 10 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3