tner-xlm-roberta-base-ontonotes5

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
  • Associated Paper
  • Space

Uses

Direct Use

Token Classification

Downstream Use [Optional]

This model can be used in conjunction with the tner library.

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) and Bender et al. (2021)). 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),

{
    '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.

{"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 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 presented in Lacoste et al. (2019).

• 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>

  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
  tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-ontonotes5")
  
  model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-ontonotes5")
 

</details>