model documentation (#2)

- model documentation (91d3c9cf234e2eaf8bcddbddd223938a04d7eff9)


Co-authored-by: Nazneen Rajani <nazneen@users.noreply.huggingface.co>

README.md

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+---
+
+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 Ousi
+- **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 Ousi 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>
+