|
--- |
|
|
|
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. |
|
|
|
<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> |
|
|
|
|
