Downloads · 30 days
7
3% of all-time downloads
hBERTv2_new_no_pretrain_sst2
gokuls/hBERTv2_new_no_pretrain_sst2
hBERTv2_new_no_pretrain_sst2 is a text classification model from gokuls. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
All-time downloads
237
Public
Repo size
6.2 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin477 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- hybridbert
- Access
- Public
- Created
- May 29, 2023
- Updated
- Jun 15, 2023
- SHA
- 56e02019
Try a prompt
Datasets
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. -->
hBERTv2_new_no_pretrain_sst2
This model is a fine-tuned version of on the GLUE SST2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4682
- Accuracy: 0.7924
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: 4e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.438 | 1.0 | 527 | 0.4682 | 0.7924 |
| 0.2599 | 2.0 | 1054 | 0.6088 | 0.7890 |
| 0.2173 | 3.0 | 1581 | 0.4971 | 0.7901 |
| 0.1972 | 4.0 | 2108 | 0.6153 | 0.7924 |
| 0.1838 | 5.0 | 2635 | 0.5523 | 0.7947 |
| 0.1812 | 6.0 | 3162 | 0.6560 | 0.8073 |
Framework versions
- Transformers 4.30.2
- Pytorch 1.14.0a0+410ce96
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- hybridbert
- Languages
- en
- Created
- May 29, 2023
- Updated
- Jun 15, 2023