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distilbert-base-uncased_fold_8_ternary_v1
elopezlopez/distilbert-base-uncased_fold_8_ternary_v1
distilbert-base-uncased_fold_8_ternary_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. --
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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
- 4435c1f4
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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_8_ternary_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: 1.8474
- F1: 0.8022
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 | 0.5398 | 0.7838 |
| 0.5509 | 2.0 | 578 | 0.6062 | 0.7703 |
| 0.5509 | 3.0 | 867 | 0.6563 | 0.7666 |
| 0.2366 | 4.0 | 1156 | 0.7688 | 0.7961 |
| 0.2366 | 5.0 | 1445 | 1.0968 | 0.7690 |
| 0.1247 | 6.0 | 1734 | 1.1414 | 0.7924 |
| 0.0482 | 7.0 | 2023 | 1.2159 | 0.7875 |
| 0.0482 | 8.0 | 2312 | 1.2703 | 0.7887 |
| 0.0245 | 9.0 | 2601 | 1.3401 | 0.7985 |
| 0.0245 | 10.0 | 2890 | 1.4645 | 0.7961 |
| 0.0149 | 11.0 | 3179 | 1.5632 | 0.7801 |
| 0.0149 | 12.0 | 3468 | 1.5249 | 0.7875 |
| 0.0124 | 13.0 | 3757 | 1.6263 | 0.7948 |
| 0.0038 | 14.0 | 4046 | 1.8059 | 0.7764 |
| 0.0038 | 15.0 | 4335 | 1.7649 | 0.7776 |
| 0.0061 | 16.0 | 4624 | 1.8293 | 0.7850 |
| 0.0061 | 17.0 | 4913 | 1.8316 | 0.7887 |
| 0.0022 | 18.0 | 5202 | 1.7628 | 0.7973 |
| 0.0022 | 19.0 | 5491 | 1.8763 | 0.7862 |
| 0.002 | 20.0 | 5780 | 1.8409 | 0.7899 |
| 0.0026 | 21.0 | 6069 | 1.8146 | 0.8022 |
| 0.0026 | 22.0 | 6358 | 1.8420 | 0.7973 |
| 0.0008 | 23.0 | 6647 | 1.8683 | 0.8010 |
| 0.0008 | 24.0 | 6936 | 1.8571 | 0.8010 |
| 0.0015 | 25.0 | 7225 | 1.8474 | 0.8022 |
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