sw-en

Model Card for Model ID

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This is a pre-trained language translation model that aims to create a translation system for English and Swahili lanuages. It is a fine-tuned version of Helsinki-NLP/opus-mt-en-swc on an unknown dataset.

Model Details

• Transformer architecture used
• Trained on a 210000 corpus pairs
• Pre-trained Helsinki-NLP/opus-mt-en-swc
• 2 models to enforce biderectional translation

Model Description

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• Developed by: Peter Rogendo, Frederick Kioko
• Model type: Transformer
• Language(s) (NLP): Transformer, Pandas, Numpy
• License: Distributed under the MIT License
• Finetuned from model [Helsinki-NLP/opus-mt-en-swc]: [This pre-trained model was re-trained on a swahili-english sentence pairs that were collected across Kenya. Swahili is the national language and is among the top three of the most spoken language in Africa. The sentences that were used to train this model were 210000 in total.]

Model Sources [optional]

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• Repository: [https://github.com/Rogendo/Eng-Swa-Translator]
• Paper [optional]:
• Demo [optional]:

Uses

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This translation model is intended to be used in many cases, from language translators, screen assistants, to even in official cases such as translating legal documents.

Direct Use

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Use a pipeline as a high-level helper

    from transformers import pipeline
    
    pipe = pipeline("text2text-generation", model="Rogendo/sw-en")

Load model directly

    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("Rogendo/sw-en")
    model = AutoModelForSeq2SeqLM.from_pretrained("Rogendo/sw-en")

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 a pipeline as a high-level helper

    from transformers import pipeline
    
    pipe = pipeline("text2text-generation", model="Rogendo/sw-en")

Load model directly

    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("Rogendo/sw-en")
    model = AutoModelForSeq2SeqLM.from_pretrained("Rogendo/sw-en")

Training Details

Training Data

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curl -X GET
"https://datasets-server.huggingface.co/rows?dataset=Rogendo%2FEnglish-Swahili-Sentence-Pairs&config=default&split=train&offset=0&length=100"

View More https://huggingface.co/datasets/Rogendo/English-Swahili-Sentence-Pairs

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: [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]

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BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

Peter Rogendo

Model Card Contact

[email protected]