# 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](https://huggingface.co/studio-ousia/luke-large?text=Paris+is+the+%3Cmask%3E+of+France.)
  - **Parent Model:** Luke
- **Resources for more information:** 
    - [GitHub Repo](https://github.com/studio-ousia/luke)
 	 - [Associated Paper](https://arxiv.org/abs/2010.01057)
 
# 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 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](https://rajpurkar.github.io/SQuAD-explorer/)** (extractive
question answering),
* **[CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/)** (named entity
recognition), **[ReCoRD](https://sheng-z.github.io/ReCoRD-explorer/)**
(cloze-style question answering),
* **[TACRED](https://nlp.stanford.edu/projects/tacred/)** (relation
classification), and
* **[Open Entity](https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html)** (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](https://rajpurkar.github.io/SQuAD-explorer/)                    | EM/F1  | **90.2**/**95.4** | 86.1/92.3 | 89.9/95.1 ([Yang et al., 2019](https://arxiv.org/abs/1906.08237))         |
| Named Entity Recognition       | [CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/)                 | F1     | **94.3**          | 93.3      | 93.5 ([Baevski et al., 2019](https://arxiv.org/abs/1903.07785))           |
| Cloze-style Question Answering | [ReCoRD](https://sheng-z.github.io/ReCoRD-explorer/)                         | EM/F1  | **90.6**/**91.2** | -         | 83.1/83.7 ([Li et al., 2019](https://www.aclweb.org/anthology/D19-6011/)) |
| Relation Classification        | [TACRED](https://nlp.stanford.edu/projects/tacred/)                          | F1     | **72.7**          | -         | 72.0 ([Wang et al. , 2020](https://arxiv.org/abs/2002.01808))             |
| Fine-grained Entity Typing     | [Open Entity](https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html) | F1     | **78.2**          | -         | 77.6 ([Wang et al. , 2020](https://arxiv.org/abs/2002.01808))             |
 
 
Please check the [Github repository](https://github.com/studio-ousia/luke) for more details and updates.
 
 
 
# Model Examination
 
More information needed
 
# Environmental Impact
 
 
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
- **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
 
* 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]
 
More information needed
 
# Model Card Authors [optional]
 
 
Studio Ousia in collaboration with Ezi Ozoani and the Hugging Face team
 
# Model Card Contact
 
More information needed
 
# How to Get Started with the Model
 
Use the code below to get started with the model.
 
<details>
<summary> Click to expand </summary>

```python
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")
```
</details>


{"license": "apache-2.0"}
transformers pytorch luke arxiv:2010.01057 arxiv:1906.08237 arxiv:1903.07785 arxiv:2002.01808 arxiv:1910.09700 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #luke #arxiv-2010.01057 #arxiv-1906.08237 #arxiv-1903.07785 #arxiv-2002.01808 #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #region-us 

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:



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
--------------------------------


More information needed


Compute Infrastructure
----------------------


More information needed


### Hardware


* transformers\_version: 4.6.0.dev0


### Software


More information needed


BibTeX:


Glossary [optional]
===================


More information needed


More Information [optional]
===========================


More information needed


Model Card Authors [optional]
=============================


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


Model Card Contact
==================


More information needed


How to Get Started with the Model
=================================


Use the code below to get started with the model.



 Click to expand 


### 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:



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
--------------------------------


More information needed


Compute Infrastructure
----------------------


More information needed ### Hardware


* transformers\_version: 4.6.0.dev0 ### Software


More information needed


BibTeX:


Glossary [optional]
===================


More information needed


More Information [optional]
===========================


More information needed


Model Card Authors [optional]
=============================


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


Model Card Contact
==================


More information needed


How to Get Started with the Model
=================================


Use the code below to get started with the model.



 Click to expand
TAGS
#transformers #pytorch #luke #arxiv-2010.01057 #arxiv-1906.08237 #arxiv-1903.07785 #arxiv-2002.01808 #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #region-us 
 ### 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:



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
--------------------------------


More information needed


Compute Infrastructure
----------------------


More information needed ### Hardware


* transformers\_version: 4.6.0.dev0 ### Software


More information needed


BibTeX:


Glossary [optional]
===================


More information needed


More Information [optional]
===========================


More information needed


Model Card Authors [optional]
=============================


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


Model Card Contact
==================


More information needed


How to Get Started with the Model
=================================


Use the code below to get started with the model.



 Click to expand
passage: TAGS
#transformers #pytorch #luke #arxiv-2010.01057 #arxiv-1906.08237 #arxiv-1903.07785 #arxiv-2002.01808 #arxiv-1910.09700 #license-apache-2.0 #endpoints_compatible #region-us 
### 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:



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
--------------------------------


More information needed


Compute Infrastructure
----------------------


More information needed### Hardware


* transformers\_version: 4.6.0.dev0### Software


More information needed


BibTeX:


Glossary [optional]
===================


More information needed


More Information [optional]
===========================


More information needed


Model Card Authors [optional]
=============================


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


Model Card Contact
==================


More information needed


How to Get Started with the Model
=================================


Use the code below to get started with the model.



 Click to expand