--- 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 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.
Click to expand ```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") ```