Downloads · 30 days
8
10% of all-time downloads
roberta-large-detect-dep
Trong-Nghia/roberta-large-detect-dep
roberta-large-detect-dep is a text classification model from Trong-Nghia. 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
79
Public
Repo size
86.7 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin1.4 GB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Jul 1, 2023
- Updated
- Jul 11, 2023
- SHA
- 7bf82af7
Try a prompt
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-detect-dep
This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6575
- Accuracy: 0.751
- F1: 0.8184
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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6351 | 1.0 | 1502 | 0.4975 | 0.783 | 0.8360 |
| 0.6114 | 2.0 | 3004 | 0.5374 | 0.744 | 0.7949 |
| 0.5377 | 3.0 | 4506 | 0.6575 | 0.751 | 0.8184 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Jul 1, 2023
- Updated
- Jul 11, 2023