Downloads · 30 days
2
6% of all-time downloads
Models-RoBERTa-1716100109.25833
leondu/Models-RoBERTa-1716100109.25833
Models-RoBERTa-1716100109.25833 is a text classification model from leondu. 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
32
Public
Parameters
125M
1.5 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors499 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- May 19, 2024
- Updated
- May 19, 2024
- SHA
- 4ec96424
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. -->
Models-RoBERTa-1716100109.25833
This model is a fine-tuned version of roberta-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6624
- Accuracy: 0.8175
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: 3e-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 |
|---|---|---|---|---|
| 0.7508 | 1.0 | 625 | 0.5231 | 0.8065 |
| 0.4183 | 2.0 | 1250 | 0.5397 | 0.8150 |
| 0.2454 | 3.0 | 1875 | 0.6624 | 0.8175 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- May 19, 2024
- Updated
- May 19, 2024