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bert_base_lda_50_v1_wnli
gokulsrinivasagan/bert_base_lda_50_v1_wnli
bert_base_lda_50_v1_wnli is a text classification model from gokulsrinivasagan. 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. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- distilbert
- Access
- Public
- Created
- Nov 26, 2024
- Updated
- Dec 4, 2024
- SHA
- 04cf75c3
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Base models
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. -->
bert_base_lda_50_v1_wnli
This model is a fine-tuned version of gokulsrinivasagan/bert_base_lda_50_v1 on the GLUE WNLI dataset. It achieves the following results on the evaluation set:
- Loss: 0.6989
- Accuracy: 0.5211
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: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.8689 | 1.0 | 3 | 0.7527 | 0.4366 |
| 0.7047 | 2.0 | 6 | 0.6994 | 0.5070 |
| 0.6931 | 3.0 | 9 | 0.6989 | 0.5211 |
| 0.6982 | 4.0 | 12 | 0.7048 | 0.4507 |
| 0.6933 | 5.0 | 15 | 0.7227 | 0.4366 |
| 0.6983 | 6.0 | 18 | 0.7109 | 0.4225 |
| 0.6998 | 7.0 | 21 | 0.7132 | 0.5211 |
| 0.6893 | 8.0 | 24 | 0.7430 | 0.4366 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- Languages
- en
- Created
- Nov 26, 2024
- Updated
- Dec 4, 2024