---
license: llama2
datasets:
- ACE05
- bc5cdr
- conll2003
- ncbi_disease
- conll2012_ontonotesv5
- rams
- tacred
- wnut_17
language:
- en
metrics:
- f1
pipeline_tag: text-generation
---
# Model Card for Model ID

GoLLIE **G**uideline-f**o**llowing **L**arge **L**anguage 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

```Python

# 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

<!-- Provide a longer summary of what this model is. -->



- **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

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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