en-sw

---

license: mit datasets:

• Rogendo/English-Swahili-Sentence-Pairs language:
• en
• sw metrics:
• accuracy library_name: transformers

---

Model Card for Rogendo/en-sw model

<!-- Provide a quick summary of what the model is/does. -->

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

<!-- Provide a longer summary of what this model is. -->

• 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]

<!-- Provide the basic links for the model. -->

• Repository: https://github.com/Rogendo/Eng-Swa-Translator

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

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

  pip install sentencepiece
  
  
  from transformers import pipeline
  model_checkpoint = "Rogendo/en-sw"
  fine_tuned_model = pipeline("translation", model=model_checkpoint)
  
  
  fine_tuned_model("Earlier today, I saw her going through the stalls in the market")

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]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

Out-of-Scope Use

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[More Information Needed]

Bias, Risks, and Limitations

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[More Information Needed]

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]

[More Information Needed]

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]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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[More Information Needed]

Factors

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[More Information Needed]

Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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 Card Authors [optional]

Peter Rogendo

Model Card Contact

[email protected]