Model Card for luke-large-finetuned-conll-2003
4
8 commits
2 linked in READMEs
updated Nov 11, 2022
LUKE (Language Understanding with Knowledge-based Embeddings) is a new pretrained contextualized representation of words and entities based on transformer.
More information needed
This model can also be used for the task of named entity recognition, cloze-style question answering, fine-grained entity typing, extractive question answering.
The model should not be used to intentionally create hostile or alienating environments for people.
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.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
More information needed
More information needed
More information needed
More information needed
LUKE achieves state-of-the-art results on five popular NLP benchmarks including
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.
More information needed
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
More information needed
More information needed
More information needed
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}
}
More information needed
More information needed
Studio Ousia in collaboration with Ezi Ozoani and the Hugging Face team
More information needed
Use the code below to get started with the model.
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")
Model Card for luke-large-finetuned-conll-2003
4
8 commits
2 linked in READMEs
updated Nov 11, 2022
LUKE (Language Understanding with Knowledge-based Embeddings) is a new pretrained contextualized representation of words and entities based on transformer.
More information needed
This model can also be used for the task of named entity recognition, cloze-style question answering, fine-grained entity typing, extractive question answering.
The model should not be used to intentionally create hostile or alienating environments for people.
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.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
More information needed
More information needed
More information needed
More information needed
LUKE achieves state-of-the-art results on five popular NLP benchmarks including
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.
More information needed
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
More information needed
More information needed
More information needed
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}
}
More information needed
More information needed
Studio Ousia in collaboration with Ezi Ozoani and the Hugging Face team
More information needed
Use the code below to get started with the model.
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")