Downloads · 30 days
8
1% of all-time downloads
finetuned_bert-base-uncased
minoosh/finetuned_bert-base-uncased
finetuned_bert-base-uncased is a text classification model from minoosh. 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
594
Public
Repo size
54.3 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin438 MB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Feb 8, 2023
- Updated
- Mar 5, 2023
- SHA
- b68530ff
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. -->
finetuned_bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1947
- Accuracy: 0.6793
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.2761 | 1.0 | 102 | 1.3225 | 0.3375 |
| 0.9847 | 2.0 | 204 | 1.0792 | 0.5509 |
| 0.6882 | 3.0 | 306 | 0.9260 | 0.6382 |
| 0.5099 | 4.0 | 408 | 0.9072 | 0.6634 |
| 0.4614 | 5.0 | 510 | 0.9115 | 0.6867 |
| 0.3406 | 6.0 | 612 | 1.0022 | 0.6751 |
| 0.189 | 7.0 | 714 | 1.0881 | 0.6751 |
| 0.2179 | 8.0 | 816 | 1.1520 | 0.6712 |
| 0.2085 | 9.0 | 918 | 1.2567 | 0.6896 |
| 0.1914 | 10.0 | 1020 | 1.2074 | 0.6828 |
| 0.1271 | 11.0 | 1122 | 1.3389 | 0.6887 |
| 0.1236 | 12.0 | 1224 | 1.3539 | 0.6790 |
| 0.0946 | 13.0 | 1326 | 1.4042 | 0.6838 |
| 0.0968 | 14.0 | 1428 | 1.4079 | 0.6877 |
| 0.1095 | 15.0 | 1530 | 1.4884 | 0.6799 |
| 0.1102 | 16.0 | 1632 | 1.5244 | 0.6790 |
| 0.1159 | 17.0 | 1734 | 1.5238 | 0.6799 |
| 0.1448 | 18.0 | 1836 | 1.5568 | 0.6780 |
| 0.1105 | 19.0 | 1938 | 1.5629 | 0.6780 |
| 0.092 | 20.0 | 2040 | 1.5588 | 0.6809 |
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
- bert
- License
- apache-2.0
- Created
- Feb 8, 2023
- Updated
- Mar 5, 2023