spanglish-upos

Model Card for Model ID

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Model Details

Model Description

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• Developed by: Benjamin Evanoff
• Funded by: National Center for Super Computing Applications
• Model type: Part-of-Speech Tagger (Transformer)
• Language(s) (NLP): Spanish/English
• License: MIT
• Finetuned from model: mBERT

Model Sources [optional]

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• Repository: https://github.com/Illinois-Linguistic-Data-Management/spanglish-pos-tagger
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

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Direct Use

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from flair.data import Sentence
from flair.models import SequenceTagger

spanglish_tagger = SequenceTagger.load('benevanoff/spanglish-upos')

example_sentence = "Caperucita Roja put rocks en el estómago de la del perro."

spanglish_tagger.predict(example_sentence)

for token in example_sentence:
    word = token.text
    upos_tag = token.labels[0] # there will only be one label per token
    print(f'The predicted UPOS tag for {word} is {upos_tag.value} with confidence of {upos_tag.score}')

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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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 recommendations.

How to Get Started with the Model

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Training Details

Training Data

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The Bilinguals in the Midwest Corpus

A subset of the Bangor Miami Corpus

Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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

• Hardware Type: AMD Ryzen 7 CPU
• Hours used: 2

Citation [optional]

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BibTeX:

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APA:

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