license: mit
datasets:
- ACE05
- bc5cdr
- conll2003
- ncbi_disease
- conll2012_ontonotesv5
- rams
- tacred
- wnut_17
- broad_twitter_corpus
- casie
- CrossNER
- e3c
- fabner
- harvey_ner
- mit_movies
- mit_restaurant
- multinerd
- WikiEvent
language:
- en
metrics:
- f1
pipeline_tag: text-generation
Model Card for Model ID
GoLLIE Guideline-following Large Language Model for IE, is a model able to improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to comply with annotation guidelines.
Model Details
# The following lines describe the task definition
@dataclass
class PersonTemplate(Template):
"""Person templates encodes the information about the given query
Person entity."""
query: str # The Person entity query
alternate_names: Optional[List[Name]] = None
"""Names used to refer to the query person that are distinct from the
'official' name. Including: aliases, stage names, abbreviations ..."""
date_of_birth: Optional[Value] = None
"""The date on which the query person was born."""
age: Optional[Value] = None
"""A reported age of the query person."""
city_of_birth: Optional[Name] = None
"""The geopolitical entity at the municipality level (city, town, or
village) in which the query person was born"""
date_of_death: Optional[Value] = None
"""The date of the query person's death."""
# This is the text to analyze
text = "Mongolian Prime Minister M. Enkhbold arrived on Monday. "
# The annotation instances that take place in the text above are listed here
result = [
PersonTemplate(
query="M. Enkhbold",
countries_of_residence=[Name("Mongolian")],
title=[String("Prime Minister")],
),
]
Model Description
- Developed by: Oscar Sainz, Iker García-Ferrero, Rodrigo Agerri, Oier Lopez de Lacalle, German Rigau, Eneko Agirre
- Institution: HiTZ Basque Center for Language Technology - Ixa, University of the Basque Country UPV/EHU
- Model type: CODE-LLaMA2
- Language(s) (NLP): English
- License: LLaMA2 License for the base and merged model. Apache 2.0 for pre-trained LoRA Adapters
- Finetuned from model [optional]: CODE-LLaMA2
Model Sources [optional]
- Repository: https://github.com/osainz59/CoLLIE
- Paper [optional]: Coming soon
- Demo [optional]: Coming soon
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]