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--- |
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library_name: transformers |
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language: |
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- en |
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base_model: gokulsrinivasagan/distilbert_lda_100_v1_book |
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tags: |
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- generated_from_trainer |
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datasets: |
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- glue |
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metrics: |
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- spearmanr |
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model-index: |
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- name: distilbert_lda_100_v1_book_stsb |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: GLUE STSB |
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type: glue |
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args: stsb |
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metrics: |
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- name: Spearmanr |
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type: spearmanr |
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value: 0.8014167289188371 |
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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_100_v1_book_stsb |
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This model is a fine-tuned version of [gokulsrinivasagan/distilbert_lda_100_v1_book](https://huggingface.co/gokulsrinivasagan/distilbert_lda_100_v1_book) on the GLUE STSB dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7981 |
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- Pearson: 0.8060 |
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- Spearmanr: 0.8014 |
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- Combined Score: 0.8037 |
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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: 5e-05 |
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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 OptimizerNames.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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| |
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| 3.1376 | 1.0 | 23 | 2.3444 | 0.1800 | 0.1657 | 0.1729 | |
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| 1.571 | 2.0 | 46 | 1.4977 | 0.6469 | 0.6552 | 0.6511 | |
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| 1.0298 | 3.0 | 69 | 0.9940 | 0.7483 | 0.7462 | 0.7472 | |
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| 0.8795 | 4.0 | 92 | 1.0649 | 0.7622 | 0.7710 | 0.7666 | |
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| 0.6951 | 5.0 | 115 | 1.5036 | 0.7508 | 0.7848 | 0.7678 | |
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| 0.5558 | 6.0 | 138 | 0.9067 | 0.7878 | 0.7914 | 0.7896 | |
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| 0.4306 | 7.0 | 161 | 0.8333 | 0.8051 | 0.8039 | 0.8045 | |
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| 0.3592 | 8.0 | 184 | 0.9582 | 0.7967 | 0.7975 | 0.7971 | |
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| 0.2847 | 9.0 | 207 | 1.0402 | 0.7929 | 0.7954 | 0.7942 | |
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| 0.2689 | 10.0 | 230 | 0.7981 | 0.8060 | 0.8014 | 0.8037 | |
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| 0.2368 | 11.0 | 253 | 0.8628 | 0.8101 | 0.8083 | 0.8092 | |
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| 0.2088 | 12.0 | 276 | 1.0529 | 0.7991 | 0.8011 | 0.8001 | |
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| 0.1912 | 13.0 | 299 | 0.8878 | 0.8011 | 0.8013 | 0.8012 | |
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| 0.1618 | 14.0 | 322 | 0.8757 | 0.7959 | 0.7943 | 0.7951 | |
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| 0.1557 | 15.0 | 345 | 0.8971 | 0.8001 | 0.7979 | 0.7990 | |
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### Framework versions |
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- Transformers 4.46.1 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.1 |
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