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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: fnet-base-Financial_Sentiment_Analysis |
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results: [] |
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language: |
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- en |
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pipeline_tag: text-classification |
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--- |
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# fnet-base-Financial_Sentiment_Analysis |
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This model is a fine-tuned version of [google/fnet-base](https://huggingface.co/google/fnet-base). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3281 |
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- Accuracy: 0.8117 |
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- Weighted f1: 0.8110 |
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- Micro f1: 0.8117 |
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- Macro f1: 0.7472 |
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- Weighted recall: 0.8117 |
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- Micro recall: 0.8117 |
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- Macro recall: 0.7394 |
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- Weighted precision: 0.8144 |
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- Micro precision: 0.8117 |
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- Macro precision: 0.7588 |
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## Model description |
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This is a sentiment analysis (text classification) model of statements about finances. |
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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 |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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## Training and evaluation data |
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Dataset Sources: |
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- https://www.kaggle.com/datasets/sbhatti/financial-sentiment-analysis |
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- https://www.kaggle.com/datasets/ankurzing/sentiment-analysis-for-financial-news |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Training results |
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| 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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
