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:
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.
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")