Model Card for Model ID

Model Details

Model Description

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]

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

Uses

Direct Use

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

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

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

The Bilinguals in the Midwest Corpus

A subset of the Bangor Miami Corpus

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

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

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]

BibTeX:

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APA:

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