model documentation

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- # XLM-RoBERTa for NER
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- XLM-RoBERTa finetuned on NER. Check more detail at [TNER repository](https://github.com/asahi417/tner).
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- ## Usage
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+ ---
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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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+
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+ XLM-RoBERTa finetuned on NER.
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+
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+ # Model Details
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+
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+ ## Model Description
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+
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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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+
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+ # Uses
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+
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+
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+ ## Direct Use
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+ Token Classification
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+
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+
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+ ## Downstream Use [Optional]
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+
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+ This model can be used in conjunction with the [tner library](https://github.com/asahi417/tner).
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+
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+ ## Out-of-Scope Use
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+
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+
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+ The model should not be used to intentionally create hostile or alienating environments for people.
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+
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+ # Bias, Risks, and Limitations
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+
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+
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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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+
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+
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+ ## Recommendations
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+
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+
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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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+
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+
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+ # Training Details
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+
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+ ## Training Data
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+
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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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+
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+
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+
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+
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+ ## Training Procedure
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+
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+ ### Preprocessing
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+
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+ More information needed
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+
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+ ### Speeds, Sizes, Times
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+
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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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+
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+ # Evaluation
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+
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+
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+ ## Testing Data, Factors & Metrics
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+
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+ ### Testing Data
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+
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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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+
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+ ### Factors
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+ More information needed
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+
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+ ### Metrics
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+
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+ More information needed
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+
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+ ## Results
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+
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+ More information needed
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+
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+ # Model Examination
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+ More information needed
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+ # Environmental Impact
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+
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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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+
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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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+
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+ # Technical Specifications [optional]
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+
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+ ## Model Architecture and Objective
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+
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+ More information needed
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+
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+ ## Compute Infrastructure
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+ More information needed
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+
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+ ### Hardware
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+
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+ More information needed
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+
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+ ### Software
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+
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+ More information needed
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+
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+ # Citation
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+
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+
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+ **BibTeX:**
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+
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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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+
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+
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+ # Glossary [optional]
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+
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+ More information needed
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+
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+ # More Information [optional]
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+ More information needed
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+
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+ # Model Card Authors [optional]
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+
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+ Asahi Ushio in collaboration with Ezi Ozoani and the Hugging Face team.
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+
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+ # Model Card Contact
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+
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+ More information needed
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+
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+ # How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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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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+ ```
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+
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+ </details>