gokulsrinivasagan
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Browse files- README.md +71 -0
- logs/events.out.tfevents.1732266925.ki-g0008.1610975.18 +2 -2
- model.safetensors +1 -1
README.md
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---
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library_name: transformers
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base_model: gokulsrinivasagan/distilbert_lda_5
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tags:
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- generated_from_trainer
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metrics:
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- matthews_correlation
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- accuracy
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model-index:
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- name: distilbert_lda_5_cola
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# distilbert_lda_5_cola
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This model is a fine-tuned version of [gokulsrinivasagan/distilbert_lda_5](https://huggingface.co/gokulsrinivasagan/distilbert_lda_5) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6184
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- Matthews Correlation: 0.0
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- Accuracy: 0.6913
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.001
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- train_batch_size: 256
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- eval_batch_size: 256
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- seed: 10
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 30
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
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| 0.9345 | 1.0 | 34 | 0.6220 | 0.0 | 0.6913 |
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| 0.6115 | 2.0 | 68 | 0.6194 | 0.0 | 0.6913 |
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| 0.6088 | 3.0 | 102 | 0.6195 | 0.0 | 0.6913 |
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| 0.6101 | 4.0 | 136 | 0.6185 | 0.0 | 0.6913 |
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| 0.6111 | 5.0 | 170 | 0.6182 | 0.0 | 0.6913 |
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| 0.6081 | 6.0 | 204 | 0.6195 | 0.0 | 0.6913 |
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| 0.6104 | 7.0 | 238 | 0.6197 | 0.0 | 0.6913 |
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| 0.6105 | 8.0 | 272 | 0.6252 | 0.0 | 0.6913 |
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| 0.6093 | 9.0 | 306 | 0.6257 | 0.0 | 0.6913 |
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| 0.6107 | 10.0 | 340 | 0.6184 | 0.0 | 0.6913 |
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### Framework versions
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- Transformers 4.46.3
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- Pytorch 2.2.1+cu118
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- Datasets 2.17.0
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- Tokenizers 0.20.3
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