Downloads · 30 days
19
5% of all-time downloads
bert-large-uncased-detect-dep-v7
Trong-Nghia/bert-large-uncased-detect-dep-v7
bert-large-uncased-detect-dep-v7 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 apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
350
Public
Repo size
14.7 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin1.3 GB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Jul 24, 2023
- Updated
- Jul 24, 2023
- SHA
- 070a7ccc
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. -->
bert-large-uncased-detect-dep-v7
This model is a fine-tuned version of bert-large-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5683
- Accuracy: 0.737
- F1: 0.8039
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: 8
- 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 | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.6273 | 1.0 | 751 | 0.5390 | 0.75 | 0.8234 |
| 0.5603 | 2.0 | 1502 | 0.5430 | 0.751 | 0.8202 |
| 0.4999 | 3.0 | 2253 | 0.5683 | 0.737 | 0.8039 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jul 24, 2023
- Updated
- Jul 24, 2023