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bert_base_lda_5_sst2
gokulsrinivasagan/bert_base_lda_5_sst2
bert_base_lda_5_sst2 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 22, 2024
- Updated
- Nov 22, 2024
- SHA
- 5ad8e05f
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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_sst2
This model is a fine-tuned version of gokulsrinivasagan/bert_base_lda_5 on the GLUE SST2 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6957
- Accuracy: 0.5092
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: 0.001
- 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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7066 | 1.0 | 264 | 0.6991 | 0.5092 |
| 0.6869 | 2.0 | 528 | 0.6957 | 0.5092 |
| 0.6868 | 3.0 | 792 | 0.6972 | 0.5092 |
| 0.6867 | 4.0 | 1056 | 0.6974 | 0.5092 |
| 0.687 | 5.0 | 1320 | 0.6972 | 0.5092 |
| 0.6873 | 6.0 | 1584 | 0.7022 | 0.5092 |
| 0.6864 | 7.0 | 1848 | 0.6988 | 0.5092 |
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 22, 2024
- Updated
- Nov 22, 2024