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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: fnet-base-Financial_Sentiment_Analysis
results: []
language:
- en
pipeline_tag: text-classification
---
# fnet-base-Financial_Sentiment_Analysis
This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base).
It achieves the following results on the evaluation set:
- Loss: 0.3281
- Accuracy: 0.8117
- Weighted f1: 0.8110
- Micro f1: 0.8117
- Macro f1: 0.7472
- Weighted recall: 0.8117
- Micro recall: 0.8117
- Macro recall: 0.7394
- Weighted precision: 0.8144
- Micro precision: 0.8117
- Macro precision: 0.7588
## Model description
This is a sentiment analysis (text classification) model of statements 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_v2.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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.6116 | 1.0 | 134 | 0.5127 | 0.6304 | 0.5606 | 0.6304 | 0.4705 | 0.6304 | 0.6304 | 0.5272 | 0.6722 | 0.6304 | 0.6103 |
| 0.4497 | 2.0 | 268 | 0.3885 | 0.7578 | 0.7490 | 0.7578 | 0.6783 | 0.7578 | 0.7578 | 0.6636 | 0.7677 | 0.7578 | 0.7196 |
| 0.3319 | 3.0 | 402 | 0.3546 | 0.7799 | 0.7784 | 0.7799 | 0.7185 | 0.7799 | 0.7799 | 0.7167 | 0.7979 | 0.7799 | 0.7440 |
| 0.2953 | 4.0 | 536 | 0.3312 | 0.8117 | 0.8105 | 0.8117 | 0.7435 | 0.8117 | 0.8117 | 0.7356 | 0.8111 | 0.8117 | 0.7532 |
| 0.2446 | 5.0 | 670 | 0.3281 | 0.8117 | 0.8110 | 0.8117 | 0.7472 | 0.8117 | 0.8117 | 0.7394 | 0.8144 | 0.8117 | 0.7588 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3 |