Downloads · 30 days
8
2% of all-time downloads
distilbert-base-uncased_finetuned_text_2_disease_cel_v1
celikmus/distilbert-base-uncased_finetuned_text_2_disease_cel_v1
distilbert-base-uncased_finetuned_text_2_disease_cel_v1 is a text classification model from celikmus. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
390
Public
Repo size
1.1 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin268 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- distilbert
- Access
- Public
- Created
- Mar 12, 2023
- Updated
- Dec 26, 2023
- SHA
- 43ecaeb0
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. -->
distilbert-base-uncased_finetuned_text_2_disease_cel_v1
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0574
- Accuracy: 0.9985
- F1: 0.9985
- Recall: 0.9985
- Precision: 0.9986
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: 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.8645 | 1.0 | 167 | 0.6333 | 0.9354 | 0.9288 | 0.9354 | 0.9501 |
| 0.1886 | 2.0 | 334 | 0.1070 | 0.9970 | 0.9970 | 0.9970 | 0.9971 |
| 0.0902 | 3.0 | 501 | 0.0574 | 0.9985 | 0.9985 | 0.9985 | 0.9986 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Mar 12, 2023
- Updated
- Dec 26, 2023