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@@ -5,15 +5,15 @@ language:
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  base_model: microsoft/phi-2
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  pipeline_tag: text-generation
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  ---
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- https://arxiv.org/abs/1710.06071
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- # Model Card for Model ID
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  ![](ft_sections.png)
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- This is a small language model designed for scientific research. It specializes in analyzing clinical trial abstracts and sorts sentences into four key sections: Background, Methods, Results, and Conclusion.
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  This makes it easier and faster for researchers to understand and organize important information from clinical studies.
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  ## Model Details
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  - **Developed by: Salvatore Saporito
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  - **Language(s) (NLP):** English
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  - **Finetuned from model:** https://huggingface.co/microsoft/phi-2
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  ## Uses
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- Automatic identification of sections in (clinical trial) abstracts.
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  ## How to Get Started with the Model
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  text = tokenizer.batch_decode(outputs,skip_special_tokens=True)[0]
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  print(text)
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  ## Training Details
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  #### Training Hyperparameters
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- bnb_config = BitsAndBytesConfig(load_in_4bit=True,
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  bnb_4bit_quant_type='nf4',
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  bnb_4bit_compute_dtype=torch.bfloat16,
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  bnb_4bit_use_double_quant=True)
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  #### Metrics
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-
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-
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- ### Results
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-
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-
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- #### Summary
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-
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  ## Technical Specifications [optional]
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  torch einops transformers bitsandbytes accelerate peft
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- ## Model Card Contact
 
 
 
 
 
 
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  base_model: microsoft/phi-2
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  pipeline_tag: text-generation
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  ---
 
 
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  ![](ft_sections.png)
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+ This is a small language model designed for scientific research application. It is fine tuned to analyzing randomized clinical trial abstracts and to classify sentences into four key sections: Background, Methods, Results, and Conclusion.
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  This makes it easier and faster for researchers to understand and organize important information from clinical studies.
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  ## Model Details
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+
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+
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  - **Developed by: Salvatore Saporito
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  - **Language(s) (NLP):** English
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  - **Finetuned from model:** https://huggingface.co/microsoft/phi-2
 
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  ## Uses
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+ Automatic identification of sections in (randomized clinical trial) abstracts.
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  ## How to Get Started with the Model
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  text = tokenizer.batch_decode(outputs,skip_special_tokens=True)[0]
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  print(text)
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+
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+ Example:
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+ Application on unseen data (
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+
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+ )
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+
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+ PROMPT: ['###Unstruct:\nKawasaki disease (KD) is a systemic vasculitis that causes abnormalities in the coronary arteries. Interleukin (IL)-41 is a novel immunoregulatory cytokine involved in the pathogenesis of some inflammatory and immune-related diseases. However, the role of IL-41 in KD is unclear. The purpose of this study was to detect the expression of IL-41 in the plasma of children with KD and its relationship with the disease.\nA total of 44 children with KD and 37 healthy controls (HC) were recruited for this study. Plasma concentrations of IL-41 were determined by ELISA. Correlations between plasma IL-41 levels and KD-related clinical parameters were analyzed by Pearson correlation and multivariate linear regression analysis. Receiver operating characteristic curve analysis was used to assess the clinical value of IL-41 in the diagnosis of KD.\nOur results showed that plasma IL-41 levels were significantly elevated in children with KD compared with HC. Correlation analysis demonstrated that IL-41 levels were positively correlated with D-dimer and N-terminal pro-B-type natriuretic peptide, and negatively correlated with IgM, mean corpuscular hemoglobin concentration, total protein, albumin and pre-albumin. Multivariable linear regression analysis revealed that IgM and mean corpuscular hemoglobin concentrations were associated with IL-41. Receiver operating characteristic curve analysis showed that the area under the curve of IL-41 was 0.7101, with IL-41 providing 88.64 % sensitivity and 54.05 % specificity.\nOur study indicated that plasma IL-41 levels in children with KD were significantly higher than those in HC, and may provide a potential diagnostic biomarker for KD.\n###Struct:\n
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+
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+ Answer Phi2_RCT1M-ft-heading:
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+ BACKGROUND: Kawasaki disease (KD) is a systemic vasculitis that causes abnormalities in the coronary arteries. Interleukin (IL)-41 is a novel immunoregulatory cytokine involved in the pathogenesis of some inflammatory and immune-related diseases. However, the role of IL-41 in KD is unclear. The purpose of this study was to detect the expression of IL-41 in the plasma of children with KD and its relationship with the disease. METHODS: A total of 44 children with KD and 37 healthy controls (HC) were recruited for this study. Plasma concentrations of IL-41 were determined by ELISA. Correlations between plasma IL-41 levels and KD-related clinical parameters were analyzed by Pearson correlation and multivariate linear regression analysis. Receiver operating characteristic curve analysis was used to assess the clinical value of IL-41 in the diagnosis of KD. RESULTS: Our results showed that plasma IL-41 levels were significantly elevated in children with KD compared with HC. Correlation analysis demonstrated that IL-41 levels were positively correlated with D-dimer and N-terminal pro-B-type natriuretic peptide, and negatively correlated with IgM, mean corpuscular hemoglobin concentration, total protein, albumin and pre-albumin. Multivariable linear regression analysis revealed that IgM and mean corpuscular hemoglobin concentrations were associated with IL-41. Receiver operating characteristic curve analysis showed that the area under the curve of IL-41 was 0.7101, with IL-41 providing 88.64 % sensitivity and 54.05 % specificity. CONCLUSIONS: Our study indicated that plasma IL-41 levels in children with KD were significantly higher than those in HC, and may provide a potential diagnostic biomarker for KD.']
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  ## Training Details
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  #### Training Hyperparameters
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+ bnb_config = BitsAndBytesConfig(load_in_4bit=True,
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  bnb_4bit_quant_type='nf4',
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  bnb_4bit_compute_dtype=torch.bfloat16,
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  bnb_4bit_use_double_quant=True)
 
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  #### Metrics
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+ Coming soon
 
 
 
 
 
 
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  ## Technical Specifications [optional]
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  torch einops transformers bitsandbytes accelerate peft
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+ ## Model Card Contact
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+
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+
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+ ## References
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+
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+ https://arxiv.org/abs/1710.06071