File size: 2,304 Bytes
bdaf98c 1bae770 bdaf98c 01387be bdaf98c df006ca bdaf98c 01387be 3752382 bdaf98c 3752382 4b32dec 3752382 bdaf98c 4b32dec bdaf98c 4b32dec bdaf98c 4b32dec bdaf98c 3752382 bdaf98c 3752382 bdaf98c 4b32dec bdaf98c 4b32dec bdaf98c 4b32dec bdaf98c 3752382 01387be 4b32dec 3752382 bdaf98c 4b32dec bdaf98c 4b32dec bdaf98c 1637b4e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
---
library_name: transformers
language:
- en
base_model: microsoft/phi-2
pipeline_tag: text-generation
---
https://arxiv.org/abs/1710.06071
# Model Card for Model ID
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.
This makes it easier and faster for researchers to understand and organize important information from clinical studies.
## Model Details
- **Developed by: Salvatore Saporito
- **Language(s) (NLP):** English
- **Finetuned from model:** https://huggingface.co/microsoft/phi-2
### Model Sources [optional]
- **Repository:** Coming soon
## Uses
Automatic identification of sections in (clinical trial) abstracts.
## How to Get Started with the Model
Prompt Format:
'''
###Unstruct:
{abstract}
###Struct:
'''
## Training Details
### Training Data
50k randomly sampled randomized clinical trial abstracts with date of pubblication within [1970-2023].
Abstracts were retrieved from MEDLINE using Biopython.
### Training Procedure
Generation of (unstructured, structured) pairs for structured abstracts.
Generation of dedicated prompt for Causal_LM modelling.
#### Training Hyperparameters
bnb_config = BitsAndBytesConfig(load_in_4bit=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True)
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
10k randomly sampled RCT abstract within period [1970-2023]
#### Metrics
### Results
#### Summary
## Technical Specifications [optional]
### Model Architecture and Objective
LoraConfig(
r=16,
lora_alpha=32,
target_modules=[
'q_proj','k_proj','v_proj','dense','fc1','fc2'],
bias="none",
lora_dropout=0.05,
task_type="CAUSAL_LM",
)
### Compute Infrastructure
#### Hardware
1 x RTX4090 - 24 GB
#### Software
torch einops transformers bitsandbytes accelerate peft
## Model Card Contact |