|
--- |
|
library_name: transformers |
|
language: |
|
- en |
|
base_model: microsoft/phi-2 |
|
pipeline_tag: text-generation |
|
--- |
|
|
|
# Model Card for Model ID |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
|
|
|
|
|
|
## Model Details |
|
|
|
### Model Description |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
|
|
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
|
|
|
- **Developed by:** [More Information Needed] |
|
- **Model type:** [More Information Needed] |
|
- **Language(s) (NLP):** English |
|
- **Finetuned from model [optional]:** https://huggingface.co/microsoft/phi-2 |
|
|
|
### Model Sources [optional] |
|
|
|
- **Repository:** Coming soon |
|
- **Paper [optional]:** [More Information Needed] |
|
- **Demo [optional]:** [More Information Needed] |
|
|
|
## 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 RCT abstract within period [1970-2023] |
|
|
|
### 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='float16', |
|
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 |
|
|
|
<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
|
|
|
[More Information Needed] |
|
|
|
### Results |
|
|
|
[More Information Needed] |
|
|
|
#### 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', |
|
], #print(model) will show the modules to use |
|
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 |
