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--- |
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tags: |
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- generated_from_trainer |
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- clip |
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- bert |
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- vision-language models |
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model-index: |
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- name: output_8_clip14_cxrbert |
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results: [] |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: feature-extraction |
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--- |
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# RCLIP (Clip model fine-tuned on radiology images and their captions) |
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This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co./openai/clip-vit-large-patch14) as an image encoder and [microsoft/BiomedVLP-CXR-BERT-general](https://huggingface.co./microsoft/BiomedVLP-CXR-BERT-general) as a text encoder on the [ROCO dataset](https://github.com/razorx89/roco-dataset). |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3388 |
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## Heatmap |
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Here is the heatmap of the similarity score of the first 30 samples on the test split of the ROCO dataset of images vs their captions: |
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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: 24 |
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- eval_batch_size: 24 |
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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: cosine |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 8.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.7951 | 0.09 | 500 | 1.1912 | |
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| 0.5887 | 0.18 | 1000 | 0.9833 | |
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| 0.5023 | 0.28 | 1500 | 0.8459 | |
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| 0.4709 | 0.37 | 2000 | 0.8479 | |
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| 0.4484 | 0.46 | 2500 | 0.7667 | |
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| 0.4319 | 0.55 | 3000 | 0.8092 | |
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| 0.4181 | 0.64 | 3500 | 0.6964 | |
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| 0.4107 | 0.73 | 4000 | 0.6463 | |
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| 0.3723 | 0.83 | 4500 | 0.7893 | |
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| 0.3746 | 0.92 | 5000 | 0.6863 | |
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| 0.3667 | 1.01 | 5500 | 0.6910 | |
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| 0.3253 | 1.1 | 6000 | 0.6863 | |
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| 0.3274 | 1.19 | 6500 | 0.6445 | |
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| 0.3065 | 1.28 | 7000 | 0.5908 | |
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| 0.2834 | 1.38 | 7500 | 0.6138 | |
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| 0.293 | 1.47 | 8000 | 0.6515 | |
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| 0.303 | 1.56 | 8500 | 0.5806 | |
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| 0.2638 | 1.65 | 9000 | 0.5587 | |
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| 0.2593 | 1.74 | 9500 | 0.5216 | |
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| 0.2451 | 1.83 | 10000 | 0.5283 | |
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| 0.2468 | 1.93 | 10500 | 0.5001 | |
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| 0.2295 | 2.02 | 11000 | 0.4975 | |
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| 0.1953 | 2.11 | 11500 | 0.4750 | |
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| 0.1954 | 2.2 | 12000 | 0.4572 | |
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| 0.1737 | 2.29 | 12500 | 0.4731 | |
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| 0.175 | 2.38 | 13000 | 0.4526 | |
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| 0.1873 | 2.48 | 13500 | 0.4890 | |
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| 0.1809 | 2.57 | 14000 | 0.4210 | |
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| 0.1711 | 2.66 | 14500 | 0.4197 | |
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| 0.1457 | 2.75 | 15000 | 0.3998 | |
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| 0.1583 | 2.84 | 15500 | 0.3923 | |
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| 0.1579 | 2.94 | 16000 | 0.3823 | |
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| 0.1339 | 3.03 | 16500 | 0.3654 | |
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| 0.1164 | 3.12 | 17000 | 0.3592 | |
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| 0.1217 | 3.21 | 17500 | 0.3641 | |
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| 0.119 | 3.3 | 18000 | 0.3553 | |
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| 0.1151 | 3.39 | 18500 | 0.3524 | |
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| 0.119 | 3.49 | 19000 | 0.3452 | |
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| 0.102 | 3.58 | 19500 | 0.3439 | |
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| 0.1085 | 3.67 | 20000 | 0.3422 | |
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| 0.1142 | 3.76 | 20500 | 0.3396 | |
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| 0.1038 | 3.85 | 21000 | 0.3392 | |
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| 0.1143 | 3.94 | 21500 | 0.3390 | |
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| 0.0983 | 4.04 | 22000 | 0.3390 | |
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| 0.0974 | 4.13 | 22500 | 0.3388 | |
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### Framework versions |
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- Transformers 4.31.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
