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README.md
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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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