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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- hyperdemocracy/usc-llm-text |
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metrics: |
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- accuracy |
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model-index: |
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- name: usclm-distilbert-base-uncased-mk1 |
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results: |
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- task: |
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name: Masked Language Modeling |
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type: fill-mask |
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dataset: |
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name: hyperdemocracy/usc-llm-text |
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type: hyperdemocracy/usc-llm-text |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.15919007666071758 |
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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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# usclm-distilbert-base-uncased-mk1 |
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This model is a fine-tuned version of [](https://huggingface.co/) on the hyperdemocracy/usc-llm-text dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 5.2971 |
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- Accuracy: 0.1592 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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