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Browse files- README.md +19 -18
- label2id.json +1 -0
- label_map.json +1 -0
- model.safetensors +1 -1
- model_head.pkl +1 -1
README.md
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- text-classification
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- generated_from_setfit_trainer
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widget:
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- text: Small pleural effusion on the left side. Mild cardiomegaly; no acute issues.
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- text: Calcified granuloma in the left lower lobe, incidental finding. Findings
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consistent with COPD exacerbation.
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- text: Right upper lobe consolidation consistent with pneumonia. Findings suggestive
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of community-acquired pneumonia.
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- text: Small pleural effusion on the left side. Suspicious for malignancy; urgent
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CT recommended.
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- text:
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metrics:
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- accuracy
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pipeline_tag: text-classification
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("setfit_model_id")
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# Run inference
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preds = model("Small pleural effusion on the left side.
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```
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<!--
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### Training Set Metrics
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| Training set | Min | Median | Max |
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| Word count |
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### Training Hyperparameters
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- batch_size: (8, 8)
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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### Framework Versions
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- Python: 3.11.11
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- text-classification
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- generated_from_setfit_trainer
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widget:
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- text: Small pleural effusion on the left side. Suspicious for malignancy; urgent
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CT recommended.
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- text: Hyperinflated lungs, consistent with COPD. No acute cardiopulmonary abnormalities.
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- text: Lungs clear, no consolidation, no tuberculosis. Normal mediastinal contour.
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Hilar structures normal. No osseous lesions. nan Normal chest radiograph. No evidence
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of infectious process. No follow-up needed. Suitable for immigration clearance.
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- text: Small pleural effusion on the left side. Normal chest X-ray, no acute findings.
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- text: Heart size and mediastinal contours normal. Lungs clear, no focal consolidation.
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0.8 cm non-calcified nodule in left upper lobe. No lymphadenopathy. No acute cardiopulmonary
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abnormalities. Follow-up CT recommended. Chest CT recommended to assess nodule.
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metrics:
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- accuracy
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pipeline_tag: text-classification
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("setfit_model_id")
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# Run inference
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preds = model("Small pleural effusion on the left side. Normal chest X-ray, no acute findings.")
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```
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<!--
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### Training Set Metrics
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| Training set | Min | Median | Max |
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| Word count | 11 | 24.3913 | 53 |
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### Training Hyperparameters
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- batch_size: (8, 8)
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:------:|:----:|:-------------:|:---------------:|
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| 0.0244 | 1 | 0.1229 | - |
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| 1.2195 | 50 | 0.1709 | - |
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| 2.4390 | 100 | 0.0923 | - |
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| 3.6585 | 150 | 0.0738 | - |
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| 4.8780 | 200 | 0.073 | - |
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| 6.0976 | 250 | 0.0634 | - |
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| 7.3171 | 300 | 0.0627 | - |
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| 8.5366 | 350 | 0.0555 | - |
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| 9.7561 | 400 | 0.0583 | - |
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### Framework Versions
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- Python: 3.11.11
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label2id.json
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{"annual screening": 0, "cardiomegaly": 1, "copd": 2, "ct recommended": 3, "malignancy": 4, "no follow-up needed": 5, "nodule": 6, "nodule (unspecified)": 7, "normal": 8, "opacity": 9, "pleural effusion": 10, "pneumonia": 11, "pneumothorax": 12, "repeat imaging": 13, "rib fracture": 14, "small nodules": 15, "suspicious nodules": 16}
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label_map.json
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{"0": "annual screening", "1": "cardiomegaly", "2": "copd", "3": "ct recommended", "4": "malignancy", "5": "no follow-up needed", "6": "nodule", "7": "nodule (unspecified)", "8": "normal", "9": "opacity", "10": "pleural effusion", "11": "pneumonia", "12": "pneumothorax", "13": "repeat imaging", "14": "rib fracture", "15": "small nodules", "16": "suspicious nodules"}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 433263448
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size 433263448
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model_head.pkl
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