Model Card for luke-large-finetuned-conll-2003

Model Details

Model Description

LUKE (Language Understanding with Knowledge-based Embeddings) is a new pretrained contextualized representation of words and entities based on transformer.

Developed by: Studio Ousia
Shared by [Optional]: More information needed
Model type: EntitySpanClassification
Language(s) (NLP): More information needed
License: Apache-2.0
Related Models: Luke-large
- Parent Model: Luke
Resources for more information:
- GitHub Repo
  - Associated Paper

Uses

Direct Use

More information needed

Downstream Use [Optional]

This model can also be used for the task of named entity recognition, cloze-style question answering, fine-grained entity typing, extractive question answering.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

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.

Training Details

Training Data

More information needed

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

More information needed

Factors

Metrics

LUKE achieves state-of-the-art results on five popular NLP benchmarks including

SQuAD v1.1 (extractive question answering),
CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering),
TACRED (relation classification), and
Open Entity (entity typing).

Results

The experimental results are provided as follows:

Task	Dataset	Metric	LUKE-large	luke-base	Previous SOTA
Extractive Question Answering	SQuAD v1.1	EM/F1	90.2/95.4	86.1/92.3	89.9/95.1 (Yang et al., 2019)
Named Entity Recognition	CoNLL-2003	F1	94.3	93.3	93.5 (Baevski et al., 2019)
Cloze-style Question Answering	ReCoRD	EM/F1	90.6/91.2	-	83.1/83.7 (Li et al., 2019)
Relation Classification	TACRED	F1	72.7	-	72.0 (Wang et al. , 2020)
Fine-grained Entity Typing	Open Entity	F1	78.2	-	77.6 (Wang et al. , 2020)

Please check the Github repository for more details and updates.

Model Examination

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

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Compute Infrastructure

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Hardware

transformers_version: 4.6.0.dev0

Software

More information needed

Citation

BibTeX:

@inproceedings{yamada2020luke,
 title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention},
 author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto},
 booktitle={EMNLP},
 year={2020}
}

Glossary [optional]

More information needed

More Information [optional]

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Model Card Authors [optional]

Studio Ousia in collaboration with Ezi Ozoani and the Hugging Face team

Model Card Contact

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How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

from transformers import AutoTokenizer, LukeForEntitySpanClassification
 
tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")
 
model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003")