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--- |
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license: cc-by-sa-4.0 |
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tags: |
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- financial-sentiment-analysis |
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- sentiment-analysis |
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- sentence_50agree |
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- generated_from_trainer |
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- sentiment |
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- finance |
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datasets: |
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- financial_phrasebank |
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- Kaggle_Self_label |
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- nickmuchi/financial-classification |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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widget: |
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- text: The USD rallied by 10% last night |
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example_title: Bullish Sentiment |
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- text: >- |
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Covid-19 cases have been increasing over the past few months impacting |
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earnings for global firms |
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example_title: Bearish Sentiment |
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- text: the USD has been trending lower |
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example_title: Mildly Bearish Sentiment |
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model-index: |
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- name: sec-bert-finetuned-finance-classification |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: financial_phrasebank |
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type: finance |
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args: sentence_50agree |
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metrics: |
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- type: F1 |
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name: F1 |
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value: 0.8744 |
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- type: accuracy |
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name: accuracy |
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value: 0.8755 |
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language: |
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- en |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# sec-bert-finetuned-finance-classification |
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This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the sentence_50Agree [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification), a dataset consisting of 4840 Financial News categorised by sentiment (negative, neutral, positive). The Kaggle dataset includes Covid-19 sentiment data and can be found here: [sentiment-classification-selflabel-dataset](https://www.kaggle.com/percyzheng/sentiment-classification-selflabel-dataset). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5277 |
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- Accuracy: 0.8755 |
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- F1: 0.8744 |
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- Precision: 0.8754 |
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- Recall: 0.8755 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 8 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.6005 | 0.99 | 71 | 0.3702 | 0.8478 | 0.8465 | 0.8491 | 0.8478 | |
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| 0.3226 | 1.97 | 142 | 0.3172 | 0.8834 | 0.8822 | 0.8861 | 0.8834 | |
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| 0.2299 | 2.96 | 213 | 0.3313 | 0.8814 | 0.8805 | 0.8821 | 0.8814 | |
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| 0.1277 | 3.94 | 284 | 0.3925 | 0.8775 | 0.8771 | 0.8770 | 0.8775 | |
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| 0.0764 | 4.93 | 355 | 0.4517 | 0.8715 | 0.8704 | 0.8717 | 0.8715 | |
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| 0.0533 | 5.92 | 426 | 0.4851 | 0.8735 | 0.8728 | 0.8731 | 0.8735 | |
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| 0.0363 | 6.9 | 497 | 0.5107 | 0.8755 | 0.8743 | 0.8757 | 0.8755 | |
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| 0.0248 | 7.89 | 568 | 0.5277 | 0.8755 | 0.8744 | 0.8754 | 0.8755 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.4 |
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- Tokenizers 0.11.6 |
