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roberta-large-financial-phrasebank-allagree1
Farshid/roberta-large-financial-phrasebank-allagree1
roberta-large-financial-phrasebank-allagree1 is a text classification model from Farshid. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Aug 8, 2022
- Updated
- Aug 9, 2022
- SHA
- 332d2064
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Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
roberta-large-financial-phrasebank-allagree1
This model is a fine-tuned version of roberta-large on the financial_phrasebank dataset. It achieves the following results on the evaluation set:
- Loss: 0.1417
- Accuracy: 0.9735
- F1: 0.9736
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.503 | 1.0 | 227 | 0.2774 | 0.9513 | 0.9517 |
| 0.177 | 2.0 | 454 | 0.1518 | 0.9779 | 0.9778 |
| 0.0789 | 3.0 | 681 | 0.1364 | 0.9823 | 0.9822 |
| 0.0512 | 4.0 | 908 | 0.1131 | 0.9779 | 0.9778 |
| 0.03 | 5.0 | 1135 | 0.1417 | 0.9735 | 0.9736 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Aug 8, 2022
- Updated
- Aug 9, 2022