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distilbert-base-uncased-finetuned-imdb-tag
sahn/distilbert-base-uncased-finetuned-imdb-tag
distilbert-base-uncased-finetuned-imdb-tag is a text classification model from sahn. 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
- May 30, 2022
- Updated
- May 30, 2022
- SHA
- d103fb07
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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. -->
distilbert-base-uncased-finetuned-imdb-tag
This model is a fine-tuned version of distilbert-base-uncased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2215
- Accuracy: 0.9672
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
For 90% of the sentences, added 10/10 at the end of the sentences with the label 1, and 1/10 with the label 0.
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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0895 | 1.0 | 1250 | 0.1332 | 0.9638 |
| 0.0483 | 2.0 | 2500 | 0.0745 | 0.9772 |
| 0.0246 | 3.0 | 3750 | 0.1800 | 0.9666 |
| 0.0058 | 4.0 | 5000 | 0.1370 | 0.9774 |
| 0.0025 | 5.0 | 6250 | 0.2215 | 0.9672 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- May 30, 2022
- Updated
- May 30, 2022