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Malay-Sentiment-4
Hanisnabila/Malay-Sentiment-4
Malay-Sentiment-4 is a text classification model from Hanisnabila. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- xlm-roberta
- Access
- Public
- Created
- Nov 14, 2024
- Updated
- Nov 14, 2024
- SHA
- e5db0cc0
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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. -->
Malay-Sentiment-4
This model is a fine-tuned version of citizenlab/twitter-xlm-roberta-base-sentiment-finetunned on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.7536
- Accuracy: 0.7543
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: 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 | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 246 | 0.6416 | 0.7188 |
| No log | 2.0 | 492 | 0.6939 | 0.7574 |
| 0.6012 | 3.0 | 738 | 0.7536 | 0.7543 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.2.2+cu118
- Datasets 3.0.1
- Tokenizers 0.20.0
- Task
- Text Classification
- Library
- transformers
- Type
- xlm-roberta
- Created
- Nov 14, 2024
- Updated
- Nov 14, 2024