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classify-sher
mufaddal-k/classify-sher
classify-sher is a text classification model from mufaddal-k. 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
- Jun 18, 2024
- Updated
- Jun 19, 2024
- SHA
- 7e8e0ebf
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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. -->
classify-sher
This model is a fine-tuned version of mufaddal-k/classify-sher on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6875
- Accuracy: 0.7512
- F1: 0.7511
- Precision: 0.7520
- Recall: 0.7512
- Accuracy Label Classical: 0.7578
- Accuracy Label Contemporary: 0.8101
- Accuracy Label Modernist: 0.6776
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3357
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Accuracy Label Classical | Accuracy Label Contemporary | Accuracy Label Modernist |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.3859 | 0.2234 | 500 | 0.7063 | 0.7195 | 0.7195 | 0.7195 | 0.7195 | 0.7609 | 0.7568 | 0.6441 |
| 0.4271 | 0.4468 | 1000 | 0.6870 | 0.7265 | 0.7218 | 0.7238 | 0.7265 | 0.7694 | 0.8353 | 0.5666 |
| 0.4572 | 0.6702 | 1500 | 0.6906 | 0.7301 | 0.7316 | 0.7347 | 0.7301 | 0.7336 | 0.7607 | 0.6918 |
| 0.4384 | 0.8937 | 2000 | 0.6800 | 0.7369 | 0.7385 | 0.7420 | 0.7369 | 0.7454 | 0.7521 | 0.7126 |
| 0.3364 | 1.1171 | 2500 | 0.7259 | 0.7452 | 0.7438 | 0.7469 | 0.7452 | 0.7313 | 0.8399 | 0.6461 |
| 0.333 | 1.3405 | 3000 | 0.6875 | 0.7512 | 0.7511 | 0.7520 | 0.7512 | 0.7578 | 0.8101 | 0.6776 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jun 18, 2024
- Updated
- Jun 19, 2024