Open fasttext LangID models

This repo makes readily available several fasttext based open langID models. While the use and distribution of this collection itself is available according to Apache 2.0, but individual models maybe under different more stringent licenses and end users MUST ensure the licenses before distribution or usage.

Quantized versions have been derived by the author Chris Ha

Model Details

Model Description

Developed by: [Individual Developers]
Shared by [optional]: [More Information Needed]
Model type: [Fasttext Classifier]
Language(s) (NLP): [Multilingual]
License: [More Information Needed]
Finetuned from model [optional]: [Individual models]

Model Source for lid.176

Repository: FastText 176
Paper : An Open Dataset and Model for Language Identification
Languages : 176
Filesize : 938013 bytes
SHA-384 : 574c310c8d82540a8765d28140bf3e6cd01ceb7d164c9ec205c518254bfa0333ae1b80aab7ceff56e7159eced98a3d37

Model Source for OpenLID

Repository: An Open Dataset and Model for Language Identification
Paper [optional]: An Open Dataset and Model for Language Identification
Languages : 201
Filesize : 158024393 bytes
SHA-384 : 99b86a2121f71fdfd801715ed19a09e5e1eee42c2f0bc6340c6aa40dd737757346733a87be130091bc01c4976973d250

Model Source for NLLB langID(lid218e)

Repository: fastText (Language Identification)
Paper [optional]: No Language Left Behind: Scaling Human-Centered Machine Translation
Demo [optional]: [More Information Needed]
Languages : 218
Filesize : 149855754 bytes
SHA-384 : a6fda88d22d2228b0beb6025ccde5da582ec8eb6679a57f4b3794b127658749653c7d6db8a17fcbe2f86c9678b20ae9f

Uses

Language Classification

Direct Use

Language Classification

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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.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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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: [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

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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