Downloads · 30 days
6
22% of all-time downloads
SETH_29_03
Brizape/SETH_29_03
SETH_29_03 is a token classification model from Brizape. 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
27
Public
Repo size
3 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin436 MB · 100%
At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Mar 29, 2023
- Updated
- Mar 29, 2023
- SHA
- f6b1c0a5
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. -->
SETH_29_03
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0866
- Precision: 0.7204
- Recall: 0.8468
- F1: 0.7785
- Accuracy: 0.9819
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-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
- training_steps: 500
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.3731 | 0.96 | 25 | 0.1863 | 0.0 | 0.0 | 0.0 | 0.9583 |
| 0.1139 | 1.92 | 50 | 0.0862 | 0.4524 | 0.6540 | 0.5348 | 0.9664 |
| 0.0654 | 2.88 | 75 | 0.0737 | 0.5914 | 0.8244 | 0.6887 | 0.9739 |
| 0.051 | 3.85 | 100 | 0.0646 | 0.6340 | 0.8227 | 0.7161 | 0.9789 |
| 0.0444 | 4.81 | 125 | 0.0769 | 0.5938 | 0.8554 | 0.7010 | 0.9732 |
| 0.031 | 5.77 | 150 | 0.0660 | 0.6541 | 0.8692 | 0.7465 | 0.9784 |
| 0.026 | 6.73 | 175 | 0.0641 | 0.7186 | 0.8262 | 0.7686 | 0.9814 |
| 0.0217 | 7.69 | 200 | 0.0682 | 0.6985 | 0.8571 | 0.7697 | 0.9813 |
| 0.0167 | 8.65 | 225 | 0.0678 | 0.7246 | 0.7969 | 0.7590 | 0.9809 |
| 0.0129 | 9.62 | 250 | 0.0727 | 0.7488 | 0.7900 | 0.7688 | 0.9825 |
| 0.0107 | 10.58 | 275 | 0.0778 | 0.7242 | 0.8451 | 0.7800 | 0.9818 |
| 0.0085 | 11.54 | 300 | 0.0784 | 0.7188 | 0.8537 | 0.7805 | 0.9820 |
| 0.0064 | 12.5 | 325 | 0.0866 | 0.7204 | 0.8468 | 0.7785 | 0.9819 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Mar 29, 2023
- Updated
- Mar 29, 2023