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Bert-Phrasebank-Sentiment-Analysis
pkbiswas/Bert-Phrasebank-Sentiment-Analysis
Bert-Phrasebank-Sentiment-Analysis is a text classification model from pkbiswas. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Oct 27, 2023
- Updated
- Oct 27, 2023
- SHA
- 16972eeb
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Base models
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. -->
phrasebank-sentiment-analysis
This model is a fine-tuned version of bert-base-uncased on the financial_phrasebank dataset. It achieves the following results on the evaluation set:
- Loss: 0.5194
- F1: 0.8447
- Accuracy: 0.8618
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 0.597 | 0.94 | 100 | 0.3968 | 0.8181 | 0.8439 |
| 0.2739 | 1.89 | 200 | 0.3645 | 0.8409 | 0.8576 |
| 0.1375 | 2.83 | 300 | 0.5283 | 0.8282 | 0.8453 |
| 0.0646 | 3.77 | 400 | 0.5194 | 0.8447 | 0.8618 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Oct 27, 2023
- Updated
- Oct 27, 2023