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license: apache-2.0 |
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# Model Card for luke-large-finetuned-conll-2003 |
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# Model Details |
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## Model Description |
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LUKE (Language Understanding with Knowledge-based Embeddings) is a new pretrained contextualized representation of words and entities based on transformer. |
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- **Developed by:** Studio Ousia |
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- **Shared by [Optional]:** More information needed |
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- **Model type:** EntitySpanClassification |
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- **Language(s) (NLP):** More information needed |
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- **License:** Apache-2.0 |
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- **Related Models:** [Luke-large](https://huggingface.co/studio-ousia/luke-large?text=Paris+is+the+%3Cmask%3E+of+France.) |
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- **Parent Model:** Luke |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/studio-ousia/luke) |
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- [Associated Paper](https://arxiv.org/abs/2010.01057) |
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# Uses |
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## Direct Use |
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More information needed |
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## Downstream Use [Optional] |
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This model can also be used for the task of named entity recognition, cloze-style question answering, fine-grained entity typing, extractive question answering. |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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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. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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More information needed |
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## Training Procedure |
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### Preprocessing |
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More information needed |
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### Speeds, Sizes, Times |
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More information needed |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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More information needed |
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### Factors |
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### Metrics |
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LUKE achieves state-of-the-art results on five popular NLP benchmarks including |
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* **[SQuAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/)** (extractive |
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question answering), |
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* **[CoNLL-2003](https://www.clips.uantwerpen.be/conll2003/ner/)** (named entity |
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recognition), **[ReCoRD](https://sheng-z.github.io/ReCoRD-explorer/)** |
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(cloze-style question answering), |
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* **[TACRED](https://nlp.stanford.edu/projects/tacred/)** (relation |
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classification), and |
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* **[Open Entity](https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html)** (entity typing). |
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## Results |
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The experimental results are provided as follows: |
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| Task | Dataset | Metric | LUKE-large | luke-base | Previous SOTA | |
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| ------------------------------ | ---------------------------------------------------------------------------- | ------ | ----------------- | --------- | ------------------------------------------------------------------------- | |
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| 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)) | |
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| 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)) | |
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| 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/)) | |
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| Relation Classification | [TACRED](https://nlp.stanford.edu/projects/tacred/) | F1 | **72.7** | - | 72.0 ([Wang et al. , 2020](https://arxiv.org/abs/2002.01808)) | |
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| 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)) | |
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Please check the [Github repository](https://github.com/studio-ousia/luke) for more details and updates. |
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# Model Examination |
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More information needed |
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# Environmental Impact |
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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). |
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- **Hardware Type:** More information needed |
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- **Hours used:** More information needed |
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- **Cloud Provider:** More information needed |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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More information needed |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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* transformers_version: 4.6.0.dev0 |
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### Software |
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More information needed |
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# Citation |
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**BibTeX:** |
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``` |
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@inproceedings{yamada2020luke, |
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title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention}, |
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author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto}, |
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booktitle={EMNLP}, |
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year={2020} |
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} |
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``` |
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# Glossary [optional] |
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More information needed |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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Studio Ousia in collaboration with Ezi Ozoani and the Hugging Face team |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, LukeForEntitySpanClassification |
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tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003") |
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model = LukeForEntitySpanClassification.from_pretrained("studio-ousia/luke-large-finetuned-conll-2003") |
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``` |
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</details> |
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