Downloads · 30 days
4
19% of all-time downloads
finetuning-emotion-model-roberta
Suryakumar-P/finetuning-emotion-model-roberta
finetuning-emotion-model-roberta is a text classification model from Suryakumar-P. 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. --
All-time downloads
21
Public
Parameters
109M
4.9 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors438 MB · 99%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Oct 22, 2024
- Updated
- Nov 10, 2024
- SHA
- 59e56e78
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. -->
finetuning-emotion-model-roberta
This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3117
- Accuracy: 0.9405
- F1: 0.9403
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 250 | 0.2637 | 0.9135 | 0.9146 |
| 0.4901 | 2.0 | 500 | 0.1735 | 0.934 | 0.9341 |
| 0.4901 | 3.0 | 750 | 0.1678 | 0.9375 | 0.9381 |
| 0.1058 | 4.0 | 1000 | 0.1603 | 0.9385 | 0.9383 |
| 0.1058 | 5.0 | 1250 | 0.1902 | 0.9345 | 0.9346 |
| 0.0684 | 6.0 | 1500 | 0.1899 | 0.9365 | 0.9367 |
| 0.0684 | 7.0 | 1750 | 0.2289 | 0.932 | 0.9313 |
| 0.0406 | 8.0 | 2000 | 0.2291 | 0.936 | 0.9361 |
| 0.0406 | 9.0 | 2250 | 0.2438 | 0.935 | 0.9353 |
| 0.0254 | 10.0 | 2500 | 0.2639 | 0.9375 | 0.9375 |
| 0.0254 | 11.0 | 2750 | 0.2863 | 0.9365 | 0.9365 |
| 0.017 | 12.0 | 3000 | 0.2814 | 0.938 | 0.9379 |
| 0.017 | 13.0 | 3250 | 0.2972 | 0.9355 | 0.9355 |
| 0.012 | 14.0 | 3500 | 0.2874 | 0.9355 | 0.9356 |
| 0.012 | 15.0 | 3750 | 0.2996 | 0.9405 | 0.9403 |
| 0.0098 | 16.0 | 4000 | 0.3110 | 0.9405 | 0.9403 |
| 0.0098 | 17.0 | 4250 | 0.3173 | 0.9365 | 0.9364 |
| 0.0082 | 18.0 | 4500 | 0.3106 | 0.9405 | 0.9404 |
| 0.0082 | 19.0 | 4750 | 0.3100 | 0.94 | 0.9398 |
| 0.006 | 20.0 | 5000 | 0.3117 | 0.9405 | 0.9403 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Oct 22, 2024
- Updated
- Nov 10, 2024