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--- |
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tags: |
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- generated_from_trainer |
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- finance |
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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: finbert-tone-finetuned-finance-text-classification |
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results: [] |
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datasets: |
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- nickmuchi/financial-text-combo-classification |
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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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# finbert-tone-finetuned-finance-text-classification |
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This model is a fine-tuned version of [yiyanghkust/finbert-tone](https://huggingface.co/yiyanghkust/finbert-tone) on the [nickmuchi/financial-text-combo-classification](https://huggingface.co/datasets/nickmuchi/financial-text-combo-classification) dataset which is a combined dataset of financial_phrasebank,FinanceInc/auditor_sentiment and zeroshot/twitter-financial-news-sentiment. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6645 |
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- Accuracy: 0.9097 |
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- F1: 0.9102 |
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- Precision: 0.9110 |
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- Recall: 0.9097 |
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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: 128 |
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- eval_batch_size: 128 |
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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: 20 |
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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 | 141 | 0.3934 | 0.8431 | 0.8427 | 0.8456 | 0.8431 | |
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| No log | 2.0 | 282 | 0.3214 | 0.8843 | 0.8843 | 0.8867 | 0.8843 | |
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| No log | 3.0 | 423 | 0.3302 | 0.8882 | 0.8902 | 0.8965 | 0.8882 | |
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| 0.4444 | 4.0 | 564 | 0.3611 | 0.8980 | 0.8993 | 0.9026 | 0.8980 | |
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| 0.4444 | 5.0 | 705 | 0.4006 | 0.8975 | 0.8987 | 0.9014 | 0.8975 | |
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| 0.4444 | 6.0 | 846 | 0.4517 | 0.9037 | 0.9043 | 0.9057 | 0.9037 | |
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| 0.4444 | 7.0 | 987 | 0.5324 | 0.9027 | 0.9035 | 0.9057 | 0.9027 | |
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| 0.0406 | 8.0 | 1128 | 0.5308 | 0.9063 | 0.9074 | 0.9098 | 0.9063 | |
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| 0.0406 | 9.0 | 1269 | 0.5586 | 0.9081 | 0.9084 | 0.9089 | 0.9081 | |
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| 0.0406 | 10.0 | 1410 | 0.5783 | 0.9076 | 0.9080 | 0.9086 | 0.9076 | |
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| 0.0121 | 11.0 | 1551 | 0.5741 | 0.9115 | 0.9116 | 0.9121 | 0.9115 | |
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| 0.0121 | 12.0 | 1692 | 0.6288 | 0.9104 | 0.9108 | 0.9115 | 0.9104 | |
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| 0.0121 | 13.0 | 1833 | 0.6328 | 0.9050 | 0.9059 | 0.9078 | 0.9050 | |
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| 0.0121 | 14.0 | 1974 | 0.6887 | 0.9042 | 0.9054 | 0.9088 | 0.9042 | |
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| 0.0063 | 15.0 | 2115 | 0.6345 | 0.9086 | 0.9094 | 0.9109 | 0.9086 | |
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| 0.0063 | 16.0 | 2256 | 0.6545 | 0.9102 | 0.9103 | 0.9108 | 0.9102 | |
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| 0.0063 | 17.0 | 2397 | 0.6585 | 0.9086 | 0.9092 | 0.9103 | 0.9086 | |
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| 0.0033 | 18.0 | 2538 | 0.6676 | 0.9081 | 0.9087 | 0.9098 | 0.9081 | |
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| 0.0033 | 19.0 | 2679 | 0.6614 | 0.9110 | 0.9113 | 0.9119 | 0.9110 | |
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| 0.0033 | 20.0 | 2820 | 0.6645 | 0.9097 | 0.9102 | 0.9110 | 0.9097 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
