readme: ---
  
  license: apache-2.0
  
  ---
  # 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 Specifi