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distilbert-distilbert-base-uncased-fine-tuned-term-typing
Carick/distilbert-distilbert-base-uncased-fine-tuned-term-typing
distilbert-distilbert-base-uncased-fine-tuned-term-typing is a text classification model from Carick. 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
- Nov 11, 2024
- Updated
- Nov 13, 2024
- SHA
- 3b29d6ab
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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. -->
distilbert-distilbert-base-uncased-fine-tuned-term-typing
This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0311
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2637 | 1.0 | 2535 | 0.0939 |
| 0.1249 | 2.0 | 5070 | 0.0467 |
| 0.0956 | 3.0 | 7605 | 0.0311 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Nov 11, 2024
- Updated
- Nov 13, 2024