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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- keyword_pubmed_dataset |
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metrics: |
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- accuracy |
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model-index: |
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- name: kw_pubmed_1000_0.0003 |
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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: keyword_pubmed_dataset |
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type: keyword_pubmed_dataset |
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args: sentence |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.33938523162661094 |
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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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# kw_pubmed_1000_0.0003 |
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the keyword_pubmed_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.7086 |
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- Accuracy: 0.3394 |
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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: 0.0003 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 250 |
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- total_train_batch_size: 8000 |
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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: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 0.09 | 4 | 4.3723 | 0.3436 | |
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| 6.0386 | 0.17 | 8 | 4.2113 | 0.3442 | |
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| 3.7573 | 0.26 | 12 | 4.2079 | 0.3634 | |
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| 2.9944 | 0.35 | 16 | 4.3370 | 0.3513 | |
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| 2.7048 | 0.44 | 20 | 4.8594 | 0.3067 | |
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| 2.7048 | 0.52 | 24 | 4.4929 | 0.3383 | |
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| 2.9458 | 0.61 | 28 | 4.5146 | 0.3408 | |
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| 2.3783 | 0.7 | 32 | 4.5680 | 0.3430 | |
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| 2.2485 | 0.78 | 36 | 4.5095 | 0.3477 | |
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| 2.1701 | 0.87 | 40 | 4.4971 | 0.3449 | |
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| 2.1701 | 0.96 | 44 | 4.7051 | 0.3321 | |
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| 2.0861 | 1.07 | 48 | 4.7615 | 0.3310 | |
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| 2.4168 | 1.15 | 52 | 4.7086 | 0.3394 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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