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my_distilbert_model
Tirendaz/my_distilbert_model
my_distilbert_model is a text classification model from Tirendaz. 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
- Aug 25, 2023
- Updated
- Sep 3, 2023
- SHA
- 1b96200b
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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. -->
my_distilbert_model
This model is a fine-tuned version of distilbert-base-uncased on the rotten_tomatoes_movie_review dataset. It achieves the following results on the evaluation set:
- Loss: 0.4418
- Accuracy: 0.8433
- F1: 0.8433
- Precision: 0.8434
- Recall: 0.8433
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: 32
- eval_batch_size: 32
- 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 | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 267 | 0.3990 | 0.8246 | 0.8243 | 0.8269 | 0.8246 |
| 0.3534 | 2.0 | 534 | 0.3951 | 0.8452 | 0.8452 | 0.8452 | 0.8452 |
| 0.3534 | 3.0 | 801 | 0.4418 | 0.8433 | 0.8433 | 0.8434 | 0.8433 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Aug 25, 2023
- Updated
- Sep 3, 2023