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market_positivity_model
leetdavid/market_positivity_model
market_positivity_model is a text classification model from leetdavid. 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.
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
- Mar 2, 2022
- Updated
- Dec 21, 2023
- SHA
- c80731c5
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Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
market_positivity_model
This model is a fine-tuned version of hfl/chinese-roberta-wwm-ext on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.5776
- Train Sparse Categorical Accuracy: 0.7278
- Validation Loss: 0.6460
- Validation Sparse Categorical Accuracy: 0.6859
- Epoch: 2
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:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|---|---|---|---|---|
| 0.7207 | 0.6394 | 0.6930 | 0.6811 | 0 |
| 0.6253 | 0.7033 | 0.6549 | 0.6872 | 1 |
| 0.5776 | 0.7278 | 0.6460 | 0.6859 | 2 |
Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Dec 21, 2023