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--- |
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license: apache-2.0 |
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base_model: NeuML/pubmedbert-base-embeddings |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: pubmed-bert-all-deep |
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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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# pubmed-bert-all-deep |
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This model is a fine-tuned version of [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9764 |
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- Precision: 0.4738 |
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- Recall: 0.4800 |
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- F1: 0.4769 |
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- Accuracy: 0.7380 |
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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: 2e-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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- 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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 363 | 1.1611 | 0.1988 | 0.1654 | 0.1806 | 0.6258 | |
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| 1.3011 | 2.0 | 726 | 1.0030 | 0.3355 | 0.3221 | 0.3287 | 0.6877 | |
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| 0.9032 | 3.0 | 1089 | 0.9300 | 0.4125 | 0.3563 | 0.3823 | 0.7095 | |
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| 0.9032 | 4.0 | 1452 | 0.8892 | 0.4466 | 0.4189 | 0.4323 | 0.7220 | |
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| 0.7036 | 5.0 | 1815 | 0.9079 | 0.4476 | 0.4530 | 0.4503 | 0.7257 | |
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| 0.5735 | 6.0 | 2178 | 0.9415 | 0.4651 | 0.4684 | 0.4667 | 0.7299 | |
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| 0.4796 | 7.0 | 2541 | 0.9484 | 0.4791 | 0.4558 | 0.4672 | 0.7324 | |
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| 0.4796 | 8.0 | 2904 | 0.9677 | 0.4673 | 0.4757 | 0.4715 | 0.7335 | |
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| 0.4197 | 9.0 | 3267 | 0.9810 | 0.4760 | 0.4791 | 0.4775 | 0.7361 | |
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| 0.3812 | 10.0 | 3630 | 0.9764 | 0.4738 | 0.4800 | 0.4769 | 0.7380 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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