Downloads · 30 days
11
21% of all-time downloads
p_model
Habaznya/p_model
p_model is a text classification model from Habaznya. 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
53
Public
Parameters
109M
1.8 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors438 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- May 4, 2024
- Updated
- May 21, 2024
- SHA
- 1c3c4039
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. -->
p_model
This model is a fine-tuned version of google-bert/bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3591
- Accuracy: 0.9066
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7455 | 1.0 | 832 | 0.4333 | 0.8517 |
| 0.3497 | 2.0 | 1664 | 0.3821 | 0.8785 |
| 0.2924 | 3.0 | 2496 | 0.3402 | 0.8955 |
| 0.2017 | 4.0 | 3328 | 0.3417 | 0.9066 |
| 0.1476 | 5.0 | 4160 | 0.3591 | 0.9066 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 4, 2024
- Updated
- May 21, 2024