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-library_name: transformers
-tags: []
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-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+---
+library_name: transformers
+license: mit
+---
 
+# Helsinki-NLP-opus-mt-ug
 
+This model translates from multiple Ugandan languages (Acoli, Luganda, Lumasaaba, Runyakore, Kiswahili) to English. It is fine-tuned from the Helsinki-NLP/opus-mt-mul-en model and has been trained and evaluated on a diverse set of multilingual datasets.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+This model translates text from multiple Ugandan languages to English. It has been fine-tuned on a dataset containing translations in Acoli, Luganda, Lumasaaba, Runyakore, and Kiswahili.
 
-### Model Sources [optional]
+- **Developed by:** Mubarak B.
+- **Model type:** Sequence-to-Sequence (Seq2Seq) model
+- **Language(s) (NLP):** Acoli (ach), Luganda (lug), Lumasaaba (lsa), Runyakore (nyn), Kiswahili (swa), English (en)
+- **License:** Apache 2.0
+- **Finetuned from model:** Helsinki-NLP/opus-mt-mul-en
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Helsinki-NLP-opus-mt-ug](https://huggingface.co/MubarakB/Helsinki-NLP-opus-mt-ug)
 
 ## 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. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+The model can be used directly for translating text from the mentioned Ugandan languages to English without further fine-tuning.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+The model can be integrated into applications requiring multilingual translation support for Ugandan languages to English.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model is not suitable for languages or domains outside those it was trained on, and it may not perform well on highly domain-specific language.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+Users should be aware that the model may inherit biases present in the training data, and it may not perform equally well across all dialects or contexts. It is recommended to validate the model's outputs in the intended use case to ensure suitability.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Users should consider additional fine-tuning or domain adaptation if using the model in a highly specialized context. Monitoring and human-in-the-loop verification are recommended for critical applications.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-[More Information Needed]
+model_name = "MubarakB/Helsinki-NLP-opus-mt-ug"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-## Training Details
+def translate(text, source_lang, target_lang):
+    tokenizer.src_lang = source_lang
+    tokenizer.tgt_lang = target_lang
+    inputs = tokenizer(text, return_tensors="pt", padding=True)
+    translated_tokens = model.generate(**inputs, max_length=128)
+    translation = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
+    return translation
 
-### Training Data
+# Example translation
+luganda_sentence = "Abantu bangi abafudde olw'endwadde z'ekikaba."
+english_translation = translate(luganda_sentence, "lug", "en")
+print("Luganda: ", luganda_sentence)
+print("English: ", english_translation)
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+## Training Details
 
-[More Information Needed]
+### Training Data
+The training data consists of a multilingual parallel corpus including Acoli, Luganda, Lumasaaba, Runyakore, and Kiswahili sentences paired with their English translations.
 
 ### Training Procedure
+**Training Regime**: FP16 mixed precision
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+**Training Hyperparameters:**
+- Batch size: 20
+- Gradient accumulation steps: 150
+- Learning rate: 2e-5
+- Epochs: 30
+- Label smoothing factor: 0.1
+- Evaluation steps interval: 10
+- Weight decay: 0.01
 
-
-#### 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
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
+### Evaluation
 
 #### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
+The testing data includes samples from all the languages mentioned in the training data section, with a focus on evaluating BLEU scores for each language.
 
 #### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+The evaluation disaggregates performance by language (Acoli, Luganda, Lumasaaba, Runyakore, Kiswahili).
 
 #### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+The primary evaluation metric used is BLEU score, which measures the quality of the translated text against reference translations.
 
 ### 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **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
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+- **Validation Loss**: 2.124478
+- **BLEU Scores**:
+  - BLEU Ach: 21.37250
+  - BLEU Lug: 58.25520
+  - BLEU Lsa: 25.23430
+  - BLEU Nyn: 49.76010
+  - BLEU Swa: 60.66220
+  - BLEU Mean: 43.05690
+
+### Model Examination
+
+#### Environmental Impact
+- **Hardware Type**: V100 GPUs
+- **Hours used**: 30 hours
+- **Cloud Provider**: [More Information Needed]
+- **Compute Region**: [More Information Needed]
+- **Carbon Emitted**: [More Information Needed]
+
+#### Technical Specifications
+
+**Model Architecture and Objective**
+The model uses a Transformer-based Seq2Seq architecture aimed at translating text from multiple source languages to English.
+
+**Compute Infrastructure**
+- **Hardware**: NVIDIA V100 GPUs
+- **Software**: PyTorch, Transformers library