Tner Xlm Roberta Base Ontonotes5

🚀 XLM-RoBERTa for NER Model Card

XLM-RoBERTa fine-tuned for Named Entity Recognition (NER), offering high - performance token classification capabilities.

🚀 Quick Start

Use the code below to get started with the model.

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")

✨ Features

• Token Classification: The model is fine - tuned for token classification tasks in the context of Named Entity Recognition.
• Compatibility: Can be used in conjunction with the tner library.

📚 Documentation

Model Details

Model Description

XLM-RoBERTa fine - tuned on NER.

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.

⚠️ Important Note

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

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

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

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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",
}

Model Card Authors [Optional]

Asahi Ushio in collaboration with Ezi Ozoani and the Hugging Face team.