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license: mit |
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tags: |
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- generated_from_trainer |
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- financial-sentiment-analysis |
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- sentiment-analysis |
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- sentence_50agree |
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- stocks |
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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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widget: |
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- text: The USD rallied by 3% last night as the Fed hiked interest rates |
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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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- text: >- |
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The USD rallied by 3% last night as the Fed hiked interest rates however, |
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higher interest rates will increase mortgage costs for homeowners |
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example_title: Neutral |
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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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model-index: |
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- name: deberta-v3-base-finetuned-finance-text-classification |
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results: [] |
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--- |
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# deberta-v3-base-finetuned-finance-text-classification |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-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: 0.7687 |
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- Accuracy: 0.8913 |
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- F1: 0.8912 |
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- Precision: 0.8927 |
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- Recall: 0.8913 |
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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: 16 |
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- eval_batch_size: 16 |
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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: 15 |
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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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| No log | 1.0 | 285 | 0.4187 | 0.8399 | 0.8407 | 0.8687 | 0.8399 | |
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| 0.5002 | 2.0 | 570 | 0.3065 | 0.8755 | 0.8733 | 0.8781 | 0.8755 | |
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| 0.5002 | 3.0 | 855 | 0.4148 | 0.8775 | 0.8775 | 0.8778 | 0.8775 | |
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| 0.1937 | 4.0 | 1140 | 0.4249 | 0.8696 | 0.8699 | 0.8719 | 0.8696 | |
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| 0.1937 | 5.0 | 1425 | 0.5121 | 0.8834 | 0.8824 | 0.8831 | 0.8834 | |
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| 0.0917 | 6.0 | 1710 | 0.6113 | 0.8775 | 0.8779 | 0.8839 | 0.8775 | |
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| 0.0917 | 7.0 | 1995 | 0.7296 | 0.8775 | 0.8776 | 0.8793 | 0.8775 | |
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| 0.0473 | 8.0 | 2280 | 0.7034 | 0.8953 | 0.8942 | 0.8964 | 0.8953 | |
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| 0.0275 | 9.0 | 2565 | 0.6995 | 0.8834 | 0.8836 | 0.8846 | 0.8834 | |
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| 0.0275 | 10.0 | 2850 | 0.7736 | 0.8755 | 0.8755 | 0.8789 | 0.8755 | |
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| 0.0186 | 11.0 | 3135 | 0.7173 | 0.8814 | 0.8814 | 0.8840 | 0.8814 | |
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| 0.0186 | 12.0 | 3420 | 0.7659 | 0.8854 | 0.8852 | 0.8873 | 0.8854 | |
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| 0.0113 | 13.0 | 3705 | 0.8415 | 0.8854 | 0.8855 | 0.8907 | 0.8854 | |
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| 0.0113 | 14.0 | 3990 | 0.7577 | 0.8953 | 0.8951 | 0.8966 | 0.8953 | |
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| 0.0074 | 15.0 | 4275 | 0.7687 | 0.8913 | 0.8912 | 0.8927 | 0.8913 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
