Downloads · 30 days
7
18% of all-time downloads
distilbert-base-uncased_fold_5_ternary
elopezlopez/distilbert-base-uncased_fold_5_ternary
distilbert-base-uncased_fold_5_ternary is a text classification model from elopezlopez. 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
39
Public
Repo size
804 MB
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
- Aug 1, 2022
- Updated
- Aug 1, 2022
- SHA
- 60fb669c
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. -->
distilbert-base-uncased_fold_5_ternary
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: 1.8096
- F1: 0.7352
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: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 291 | 0.6322 | 0.6742 |
| 0.5533 | 2.0 | 582 | 0.5861 | 0.7285 |
| 0.5533 | 3.0 | 873 | 0.6893 | 0.7117 |
| 0.2576 | 4.0 | 1164 | 1.0393 | 0.7124 |
| 0.2576 | 5.0 | 1455 | 1.1506 | 0.6988 |
| 0.1097 | 6.0 | 1746 | 1.3005 | 0.7166 |
| 0.0487 | 7.0 | 2037 | 1.5242 | 0.7124 |
| 0.0487 | 8.0 | 2328 | 1.5705 | 0.7010 |
| 0.0253 | 9.0 | 2619 | 1.5180 | 0.7194 |
| 0.0253 | 10.0 | 2910 | 1.6251 | 0.7062 |
| 0.022 | 11.0 | 3201 | 1.6299 | 0.7169 |
| 0.022 | 12.0 | 3492 | 1.7322 | 0.7091 |
| 0.0065 | 13.0 | 3783 | 1.8441 | 0.7044 |
| 0.0093 | 14.0 | 4074 | 1.9063 | 0.7097 |
| 0.0093 | 15.0 | 4365 | 1.8096 | 0.7352 |
| 0.0037 | 16.0 | 4656 | 1.8589 | 0.7321 |
| 0.0037 | 17.0 | 4947 | 1.9687 | 0.7211 |
| 0.0036 | 18.0 | 5238 | 1.9244 | 0.7285 |
| 0.0045 | 19.0 | 5529 | 1.9835 | 0.7299 |
| 0.0045 | 20.0 | 5820 | 2.0766 | 0.7139 |
| 0.0024 | 21.0 | 6111 | 2.1118 | 0.7144 |
| 0.0024 | 22.0 | 6402 | 2.0544 | 0.7197 |
| 0.0006 | 23.0 | 6693 | 2.0914 | 0.7217 |
| 0.0006 | 24.0 | 6984 | 2.1028 | 0.7195 |
| 0.0006 | 25.0 | 7275 | 2.1174 | 0.7224 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Aug 1, 2022
- Updated
- Aug 1, 2022