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finetuned_model_on-4k-samples
Wolverine001/finetuned_model_on-4k-samples
finetuned_model_on-4k-samples is a text classification model from Wolverine001. 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
- Jul 21, 2024
- Updated
- Jul 21, 2024
- SHA
- 903ec559
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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. -->
finetuned_model_on-4k-samples
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5070
- Accuracy: 0.8379
- F1 Macro: 0.8347
- F1 Weighted: 0.8380
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|---|---|---|---|---|---|---|
| 0.5616 | 1.0 | 350 | 0.5412 | 0.795 | 0.7912 | 0.7947 |
| 0.3281 | 2.0 | 700 | 0.5039 | 0.825 | 0.8201 | 0.8246 |
| 0.1472 | 3.0 | 1050 | 0.5070 | 0.8379 | 0.8347 | 0.8380 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Jul 21, 2024
- Updated
- Jul 21, 2024