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roberta-base-finetuned-sentiment
luluw/roberta-base-finetuned-sentiment
roberta-base-finetuned-sentiment is a text classification model from luluw. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Nov 18, 2024
- Updated
- Nov 18, 2024
- SHA
- 3719ee50
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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. -->
roberta-base-finetuned-sentiment
This model is a fine-tuned version of roberta-base on the imdb-dataset-of-50k-movie-reviews dataset. It achieves the following results on the evaluation set:
- Loss: 0.2595
- Accuracy: 0.9495
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2815 | 1.0 | 1250 | 0.1705 | 0.9366 |
| 0.1358 | 2.0 | 2500 | 0.1550 | 0.9463 |
| 0.0879 | 3.0 | 3750 | 0.2081 | 0.947 |
| 0.0564 | 4.0 | 5000 | 0.2479 | 0.9474 |
| 0.0339 | 5.0 | 6250 | 0.2595 | 0.9495 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Languages
- en
- Created
- Nov 18, 2024
- Updated
- Nov 18, 2024