Downloads · 30 days
2
1% of all-time downloads
roberta-large-finetuned-t_feedback
Gregorig/roberta-large-finetuned-t_feedback
roberta-large-finetuned-t_feedback is a text classification model from Gregorig. 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
291
Public
Parameters
355M
1.4 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors1.4 GB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jun 5, 2024
- Updated
- Jun 5, 2024
- SHA
- dcfba14f
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. -->
roberta-large-finetuned-t_feedback
This model is a fine-tuned version of roberta-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0330
- Accuracy: 0.995
- F1: 0.9955
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: 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 | F1 |
|---|---|---|---|---|---|
| 0.6787 | 1.0 | 51 | 0.9594 | 0.99 | 0.9850 |
| 0.842 | 2.0 | 102 | 0.4377 | 0.965 | 0.9724 |
| 0.4738 | 3.0 | 153 | 0.0330 | 0.995 | 0.9955 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jun 5, 2024
- Updated
- Jun 5, 2024