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finetuning-sentiment-model-1000-samples-imdb-v1
ParhamAbdarzade/finetuning-sentiment-model-1000-samples-imdb-v1
finetuning-sentiment-model-1000-samples-imdb-v1 is a text classification model from ParhamAbdarzade. 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
- Dec 4, 2022
- Updated
- Dec 4, 2022
- SHA
- 194b3292
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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. -->
finetuning-sentiment-model-1000-samples-imdb-v1
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: 0.7453
- Accuracy: 0.8667
- F1: 0.8701
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: 8
- eval_batch_size: 8
- 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 | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 125 | 0.4510 | 0.88 | 0.8816 |
| No log | 2.0 | 250 | 0.5184 | 0.8833 | 0.8736 |
| No log | 3.0 | 375 | 0.5525 | 0.8967 | 0.8920 |
| 0.1 | 4.0 | 500 | 0.5790 | 0.8967 | 0.8912 |
| 0.1 | 5.0 | 625 | 0.5501 | 0.8967 | 0.8927 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Dec 4, 2022
- Updated
- Dec 4, 2022