Downloads · 30 days
3
1% of all-time downloads
roBERTa-3epoch
troesy/roBERTa-3epoch
roBERTa-3epoch is a token classification model from troesy. Use it when you need labels on individual words, such as names. 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
339
Public
Repo size
1.5 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin496 MB · 99%
At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Nov 8, 2022
- Updated
- Nov 8, 2022
- SHA
- 36b07cb5
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-3epoch
This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1328
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.9526
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: 8
- 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 | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 174 | 0.1814 | 0.0 | 0.0 | 0.0 | 0.9318 |
| No log | 2.0 | 348 | 0.1445 | 0.0 | 0.0 | 0.0 | 0.9477 |
| 0.214 | 3.0 | 522 | 0.1328 | 0.0 | 0.0 | 0.0 | 0.9526 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- mit
- Created
- Nov 8, 2022
- Updated
- Nov 8, 2022