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--- |
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language: |
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- en |
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--- |
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# Model Card for XLM-RoBERTa for NER |
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XLM-RoBERTa finetuned on NER. |
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# Model Details |
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## Model Description |
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XLM-RoBERTa finetuned on NER. |
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- **Developed by:** Asahi Ushio |
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- **Shared by [Optional]:** Hugging Face |
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- **Model type:** Token Classification |
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- **Language(s) (NLP):** en |
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- **License:** More information needed |
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- **Related Models:** XLM-RoBERTa |
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- **Parent Model:** XLM-RoBERTa |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/asahi417/tner) |
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- [Associated Paper](https://arxiv.org/abs/2209.12616) |
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- [Space](https://huggingface.co/spaces/akdeniz27/turkish-named-entity-recognition) |
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# Uses |
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## Direct Use |
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Token Classification |
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## Downstream Use [Optional] |
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This model can be used in conjunction with the [tner library](https://github.com/asahi417/tner). |
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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 recomendations. |
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# Training Details |
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## Training Data |
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An NER dataset contains a sequence of tokens and tags for each split (usually `train`/`validation`/`test`), |
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```python |
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{ |
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'train': { |
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'tokens': [ |
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['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.'], |
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['From', 'Green', 'Newsfeed', ':', 'AHFA', 'extends', 'deadline', 'for', 'Sage', 'Award', 'to', 'Nov', '.', '5', 'http://tinyurl.com/24agj38'], ... |
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], |
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'tags': [ |
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0], |
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[0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ... |
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] |
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}, |
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'validation': ..., |
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'test': ..., |
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} |
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``` |
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with a dictionary to map a label to its index (`label2id`) as below. |
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```python |
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{"O": 0, "B-ORG": 1, "B-MISC": 2, "B-PER": 3, "I-PER": 4, "B-LOC": 5, "I-ORG": 6, "I-MISC": 7, "I-LOC": 8} |
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``` |
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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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**Layer_norm_eps:** 1e-05, |
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**Num_attention_heads:** 12, |
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**Num_hidden_layers:** 12, |
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**Vocab_size:** 250002 |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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See [dataset card](https://github.com/asahi417/tner/blob/master/DATASET_CARD.md) for full dataset lists |
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### Factors |
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More information needed |
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### Metrics |
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More information needed |
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## Results |
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More information needed |
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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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More information needed |
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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{ushio-camacho-collados-2021-ner, |
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title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition", |
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author = "Ushio, Asahi and |
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Camacho-Collados, Jose", |
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booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations", |
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month = apr, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/2021.eacl-demos.7", |
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pages = "53--62", |
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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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Asahi Ushio 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, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-ontonotes5") |
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model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-ontonotes5") |
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``` |
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</details> |
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