Open fasttext LangID models
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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
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- 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
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- Repository: FastText 176
- Paper : An Open Dataset and Model for Language Identification
- Languages : 176
- Filesize : 938013 bytes
- SHA-384 : 574c310c8d82540a8765d28140bf3e6cd01ceb7d164c9ec205c518254bfa0333ae1b80aab7ceff56e7159eced98a3d37
Model Source for OpenLID
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- 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)
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- 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
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> Language Classification
Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> 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
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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
Use the code below to get started with the model.
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Training Details
Training Data
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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).
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- Hours used: [More Information Needed]
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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]
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BibTeX:
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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