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--- |
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license: apache-2.0 |
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language: "en" |
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tags: |
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- financial-sentiment-analysis |
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- sentiment-analysis |
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- generated_from_trainer |
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- financial |
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- stocks |
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- sentiment |
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metrics: |
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- f1 |
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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: "Covid-19 cases have been increasing over the past few months" |
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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: distilroberta-finetuned-finclass |
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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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datasets: |
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name: financial_phrasebank |
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type: financial_phrasebank |
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args: sentences_allagree |
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name_1: Kaggle Self label |
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type_1: financial-classification |
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metrics: |
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name: F1-Score |
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type: F1-Score |
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value: 0.88 |
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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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# distilroberta-finetuned-finclass |
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This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the [financial-phrasebank + Kaggle Dataset](https://huggingface.co/datasets/nickmuchi/financial-classification). 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: 0.4463 |
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- F1: 0.8835 |
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## Model description |
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Model determines the financial sentiment of given text. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance. |
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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: 10 |
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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 | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.7309 | 1.0 | 72 | 0.3671 | 0.8441 | |
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| 0.3757 | 2.0 | 144 | 0.3199 | 0.8709 | |
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| 0.3054 | 3.0 | 216 | 0.3096 | 0.8678 | |
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| 0.2229 | 4.0 | 288 | 0.3776 | 0.8390 | |
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| 0.1744 | 5.0 | 360 | 0.3678 | 0.8723 | |
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| 0.1436 | 6.0 | 432 | 0.3728 | 0.8758 | |
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| 0.1044 | 7.0 | 504 | 0.4116 | 0.8744 | |
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| 0.0931 | 8.0 | 576 | 0.4148 | 0.8761 | |
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| 0.0683 | 9.0 | 648 | 0.4423 | 0.8837 | |
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| 0.0611 | 10.0 | 720 | 0.4463 | 0.8835 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.0 |
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- Tokenizers 0.10.3 |
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