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bert_base_lda_5_v1_rte
gokulsrinivasagan/bert_base_lda_5_v1_rte
bert_base_lda_5_v1_rte 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
- aea5efaa
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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_5_v1_rte
This model is a fine-tuned version of gokulsrinivasagan/bert_base_lda_5_v1 on the GLUE RTE dataset. It achieves the following results on the evaluation set:
- Loss: 0.6868
- Accuracy: 0.5451
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 |
|---|---|---|---|---|
| 0.7078 | 1.0 | 10 | 0.6900 | 0.5379 |
| 0.6627 | 2.0 | 20 | 0.6868 | 0.5451 |
| 0.5816 | 3.0 | 30 | 0.7390 | 0.5415 |
| 0.4535 | 4.0 | 40 | 0.8692 | 0.5451 |
| 0.3048 | 5.0 | 50 | 1.0894 | 0.5235 |
| 0.1715 | 6.0 | 60 | 1.3816 | 0.5126 |
| 0.0964 | 7.0 | 70 | 1.6369 | 0.5451 |
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