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scenario-TCR_data-AmazonScience_massive_all_1_1
haryoaw/scenario-TCR_data-AmazonScience_massive_all_1_1
scenario-TCR_data-AmazonScience_massive_all_1_1 is a text classification model from haryoaw. 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
- deberta-v2
- Access
- Public
- Created
- Mar 3, 2024
- Updated
- Jul 27, 2024
- SHA
- 13f7b331
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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. -->
scenario-TCR_data-AmazonScience_massive_all_1_1
This model is a fine-tuned version of microsoft/mdeberta-v3-base on the massive dataset. It achieves the following results on the evaluation set:
- Loss: 0.9483
- Accuracy: 0.8559
- F1: 0.8319
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: 32
- seed: 66
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.519 | 0.27 | 5000 | 0.6915 | 0.8379 | 0.7941 |
| 0.3806 | 0.53 | 10000 | 0.6969 | 0.8468 | 0.8063 |
| 0.3259 | 0.8 | 15000 | 0.6916 | 0.8515 | 0.8159 |
| 0.2379 | 1.07 | 20000 | 0.7826 | 0.8505 | 0.8191 |
| 0.236 | 1.34 | 25000 | 0.7514 | 0.8508 | 0.8189 |
| 0.2298 | 1.6 | 30000 | 0.7719 | 0.8526 | 0.8267 |
| 0.2169 | 1.87 | 35000 | 0.8162 | 0.8505 | 0.8265 |
| 0.164 | 2.14 | 40000 | 0.8316 | 0.8549 | 0.8272 |
| 0.1684 | 2.41 | 45000 | 0.8123 | 0.8513 | 0.8204 |
| 0.158 | 2.67 | 50000 | 0.8252 | 0.8556 | 0.8309 |
| 0.1761 | 2.94 | 55000 | 0.8092 | 0.8545 | 0.8287 |
| 0.1378 | 3.21 | 60000 | 0.8574 | 0.8607 | 0.8357 |
| 0.1399 | 3.47 | 65000 | 0.8976 | 0.8572 | 0.8359 |
| 0.1431 | 3.74 | 70000 | 0.8908 | 0.8536 | 0.8350 |
| 0.1249 | 4.01 | 75000 | 0.9613 | 0.8533 | 0.8292 |
| 0.1129 | 4.28 | 80000 | 0.9511 | 0.8543 | 0.8306 |
| 0.1143 | 4.54 | 85000 | 0.9001 | 0.8563 | 0.8331 |
| 0.122 | 4.81 | 90000 | 0.9483 | 0.8559 | 0.8319 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Created
- Mar 3, 2024
- Updated
- Jul 27, 2024