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