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--- |
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library_name: transformers |
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language: |
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- en |
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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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{width=50%} |
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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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### Model Sources [optional] |
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- **Repository:** Coming soon |
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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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Prompt Format: |
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''' |
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###Unstruct: |
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{abstract} |
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###Struct: |
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''' |
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## Training Details |
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### Training Data |
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50k randomly sampled randomized clinical trial abstracts with date of pubblication within [1970-2023]. |
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Abstracts were retrieved from MEDLINE using Biopython. |
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### Training Procedure |
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Generation of (unstructured, structured) pairs for structured abstracts. |
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Generation of dedicated prompt for Causal_LM modelling. |
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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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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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10k randomly sampled RCT abstract within period [1970-2023] |
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#### Metrics |
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### Results |
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#### Summary |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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LoraConfig( |
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r=16, |
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lora_alpha=32, |
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target_modules=[ |
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'q_proj','k_proj','v_proj','dense','fc1','fc2'], |
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bias="none", |
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lora_dropout=0.05, |
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task_type="CAUSAL_LM", |
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) |
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### Compute Infrastructure |
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#### Hardware |
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1 x RTX4090 - 24 GB |
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#### Software |
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torch einops transformers bitsandbytes accelerate peft |
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## Model Card Contact |
