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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- financial_phrasebank |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: roberta-large-financial-phrasebank-allagree1 |
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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: financial_phrasebank |
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config: sentences_allagree |
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split: train |
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args: sentences_allagree |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9734513274336283 |
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- name: F1 |
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type: f1 |
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value: 0.9736033872259027 |
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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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# roberta-large-financial-phrasebank-allagree1 |
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This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the financial_phrasebank dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1417 |
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- Accuracy: 0.9735 |
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- F1: 0.9736 |
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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: 8 |
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- eval_batch_size: 8 |
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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 | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 0.503 | 1.0 | 227 | 0.2774 | 0.9513 | 0.9517 | |
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| 0.177 | 2.0 | 454 | 0.1518 | 0.9779 | 0.9778 | |
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| 0.0789 | 3.0 | 681 | 0.1364 | 0.9823 | 0.9822 | |
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| 0.0512 | 4.0 | 908 | 0.1131 | 0.9779 | 0.9778 | |
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| 0.03 | 5.0 | 1135 | 0.1417 | 0.9735 | 0.9736 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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