Downloads · 30 days
3
4% of all-time downloads
sentiment-model
ishanevatia/sentiment-model
sentiment-model is a text classification model from ishanevatia. 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. --
All-time downloads
76
Public
Parameters
4.4M
70.2 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors17.5 MB · 95%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Nov 14, 2025
- Updated
- Nov 19, 2025
- SHA
- d2d3cbe6
Try a prompt
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. -->
sentiment-model
This model is a fine-tuned version of prajjwal1/bert-tiny on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4629
- Accuracy: 0.815
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 313 | 0.5173 | 0.783 |
| 0.5861 | 2.0 | 626 | 0.4629 | 0.815 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Nov 14, 2025
- Updated
- Nov 19, 2025