allenai/scibert_scivocab_uncased_field_of_study
allenai
Clasificación de texto
Field of study pytorch model es una versión afinada de allenai/scibert_scivocab_uncased. Los datos para la afinación se recopilaron utilizando modelos de OpenAI con el siguiente prompt.
Como usar
def prompt_with_journal(title, abstract, journal_name):
message = [
{"role": "system", "content": "You are a highly intelligent and accurate information extraction system. You take title, abstract, journal name of a scientific article as input and your task is to classify the scientific field of study of the passage.",
"role": "user", "content": "You need to classify it with key: 'field_of_study' assign as many 'field_of_study' as you find it fit: 'Agricultural and Food sciences', 'Art', 'Biology', 'Business', 'Chemistry', 'Computer science', 'Economics', 'Education', 'Engineering', 'Environmental science', 'Geography', 'Geology', 'History', 'Law', 'Linguistics', 'Materials science', 'Mathematics', 'Medicine', 'Philosophy', 'Physics', 'Political science', 'Psychology', 'Sociology' Only select from the above list, or 'Other'."},
{"role": "assistant", "content": ("```python\ntitle = { title } \nabstract = { abstract }\njournal_name = { journal_name }\n{"field_of_study": ["},]}
return message
Funcionalidades
- Clasificación de texto
- Transformers
- PyTorch
- Endpoints de inferencia
Casos de uso
- Clasificación del campo de estudio de artículos científicos
- Extracción de información precisa y exacta