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pharma_classification_v2
NikhilBITS/pharma_classification_v2
pharma_classification_v2 is a text classification model from NikhilBITS. 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
- distilbert
- Access
- Public
- Created
- Mar 25, 2024
- Updated
- Mar 25, 2024
- SHA
- e5158db4
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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 the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
pharma_classification
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5315
- Accuracy: 0.9581
- F1: 0.9506
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 30000
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.0035 | 5.99 | 5000 | 0.2892 | 0.9539 | 0.9554 |
| 0.0137 | 11.98 | 10000 | 0.2620 | 0.9641 | 0.9600 |
| 0.0 | 17.96 | 15000 | 0.4022 | 0.9611 | 0.9586 |
| 0.0001 | 23.95 | 20000 | 0.3838 | 0.9611 | 0.9552 |
| 0.0 | 29.94 | 25000 | 0.4363 | 0.9575 | 0.9490 |
| 0.0 | 35.93 | 30000 | 0.5315 | 0.9581 | 0.9506 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 25, 2024
- Updated
- Mar 25, 2024