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tamil-sentiment-distilbert
Vasanth/tamil-sentiment-distilbert
tamil-sentiment-distilbert is a text classification model from Vasanth. 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
- Mar 2, 2022
- Updated
- Aug 23, 2021
- SHA
- 7016d9d2
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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. -->
tamil-sentiment-distilbert
This model is a fine-tuned version of distilbert-base-cased on the tamilmixsentiment dataset. It achieves the following results on the evaluation set:
- Loss: 1.0230
- Accuracy: 0.665
Dataset Information
- text: Tamil-English code-mixed comment.
- label: list of the possible sentiments
- LABEL_0: "Positive",
- LABEL_1: "Negative",
- LABEL_2: "Mixed_feelings",
- LABEL_3: "unknown_state",
- LABEL_4: "not-Tamil"
Intended uses & limitations
This model was just created for doing classification task on tamilmixsentiment dataset
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0442 | 1.0 | 250 | 0.9883 | 0.674 |
| 0.9227 | 2.0 | 500 | 0.9782 | 0.673 |
| 0.7591 | 3.0 | 750 | 1.0230 | 0.665 |
Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Aug 23, 2021