Create README.md

README.md

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+---
+license: cc-by-2.0
+datasets:
+- mbazaNLP/NMT_Tourism_parallel_data_en_kin
+- mbazaNLP/NMT_Education_parallel_data_en_kin
+- mbazaNLP/Kinyarwanda_English_parallel_dataset
+language:
+- en
+- rw
+library_name: transformers
+
+pipeline_tag: translation
+---
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is a Machine Translation model, finetuned from [NLLB](https://huggingface.co/facebook/nllb-200-distilled-1.3B)-200's distilled 1.3B model, it is meant to be used in machine translation for tourism-related data, in a Rwandan context.
+
+- **Finetuning code repository:** the code used to finetune this model can be found [here](https://github.com/Digital-Umuganda/twb_nllb_finetuning)
+
+## Quantization details
+
+The model is quantized to 8-bit precision using the Ctranslate2 library.
+```
+pip install ctranslate2
+```
+Using the command: 
+```
+ct2-transformers-converter --model <model-dir> --quantization int8 --output_dir <output-model-dir>
+```
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+
+### Training Procedure 
+
+The model was finetuned on three datasets; a [general](https://huggingface.co/datasets/mbazaNLP/Kinyarwanda_English_parallel_dataset) purpose dataset, a [tourism](https://huggingface.co/datasets/mbazaNLP/NMT_Tourism_parallel_data_en_kin), and an [education](https://huggingface.co/datasets/mbazaNLP/NMT_Education_parallel_data_en_kin) dataset.
+
+The model was finetuned in two phases.
+
+#### Phase one:
+- General purpose dataset
+- Education dataset
+- Tourism dataset
+
+#### Phase two:
+- Tourism dataset
+
+Other than the dataset changes between phase one, and phase two finetuning; no other hyperparameters were modified. In both cases, the model was trained on an A100 40GB GPU for two epochs.
+
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+
+
+<!-- This should link to a Data Card if possible. -->
+
+
+#### Metrics
+
+Model performance was measured using BLEU, spBLEU, TER, and chrF++ metrics.
+
+### Results