Downloads · 30 days
6
18% of all-time downloads
distilbert-base-uncased_fold_4_binary_v1
elopezlopez/distilbert-base-uncased_fold_4_binary_v1
distilbert-base-uncased_fold_4_binary_v1 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
33
Public
Repo size
536 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 2, 2022
- Updated
- Aug 2, 2022
- SHA
- c66f6694
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_4_binary_v1
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: 1.5144
- F1: 0.8245
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 | 289 | 0.3756 | 0.8175 |
| 0.3977 | 2.0 | 578 | 0.3672 | 0.8336 |
| 0.3977 | 3.0 | 867 | 0.4997 | 0.8276 |
| 0.1972 | 4.0 | 1156 | 0.6597 | 0.8244 |
| 0.1972 | 5.0 | 1445 | 0.8501 | 0.8195 |
| 0.0824 | 6.0 | 1734 | 1.0074 | 0.8097 |
| 0.037 | 7.0 | 2023 | 1.1122 | 0.8131 |
| 0.037 | 8.0 | 2312 | 1.0963 | 0.8189 |
| 0.0182 | 9.0 | 2601 | 1.2511 | 0.8125 |
| 0.0182 | 10.0 | 2890 | 1.2255 | 0.8141 |
| 0.0121 | 11.0 | 3179 | 1.3120 | 0.8187 |
| 0.0121 | 12.0 | 3468 | 1.4182 | 0.8165 |
| 0.0079 | 13.0 | 3757 | 1.4142 | 0.8218 |
| 0.0081 | 14.0 | 4046 | 1.4765 | 0.8150 |
| 0.0081 | 15.0 | 4335 | 1.3510 | 0.8187 |
| 0.0109 | 16.0 | 4624 | 1.3455 | 0.8255 |
| 0.0109 | 17.0 | 4913 | 1.4157 | 0.8234 |
| 0.0022 | 18.0 | 5202 | 1.4651 | 0.8197 |
| 0.0022 | 19.0 | 5491 | 1.4388 | 0.8267 |
| 0.0017 | 20.0 | 5780 | 1.4552 | 0.8304 |
| 0.0005 | 21.0 | 6069 | 1.5357 | 0.8248 |
| 0.0005 | 22.0 | 6358 | 1.4924 | 0.8241 |
| 0.0009 | 23.0 | 6647 | 1.4865 | 0.8248 |
| 0.0009 | 24.0 | 6936 | 1.4697 | 0.8275 |
| 0.0013 | 25.0 | 7225 | 1.5144 | 0.8245 |
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 2, 2022
- Updated
- Aug 2, 2022