Update README.md

README.md

@@ -148,11 +148,29 @@ This model is provided with its PyTorch state dictionary, tokenizer files, and c
 * **Data Source Bias:** Biases in `kambale/luganda-english-parallel-corpus` will be reflected.
 * **Generalization:** May not generalize well to very different domains.
 
-## Ethical Considerations
-(Content similar to original)
+## Limitations and Bias
+* Low-Resource Pair: Luganda is a low-resource language. While the kambale/luganda-english-parallel-corpus is a valuable asset, the overall volume of parallel data is still limited compared to high-resource language pairs. This can lead to:
+* Difficulties in handling out-of-vocabulary (OOV) words or rare phrases.
+* Potential for translations to be less fluent or accurate for complex sentences or nuanced expressions.
+* The model might reflect biases present in the training data.
+* Data Source Bias: The characteristics and biases of the kambale/luganda-english-parallel-corpus (e.g., domain, style, demographic representation) will be reflected in the model's translations.
+* Generalization: The model may not generalize well to domains significantly different from the training data.
+* No Back-translation or Advanced Techniques: This model was trained directly on the parallel corpus without more advanced techniques like back-translation or pre-training on monolingual data, which could further improve performance.
+* Greedy Decoding for Examples: Performance metrics (BLEU) are typically calculated using beam search. The conceptual usage examples might rely on greedy decoding, which can be suboptimal.
 
-## Future Work
-(Content similar to original, e.g., beam search, larger datasets/models)
+## Ethical Considerations
+* Bias Amplification: Machine translation models can inadvertently perpetuate or even amplify societal biases present in the training data. Users should be aware of this potential when using the translations.
+* Misinformation: As with any generative model, there's a potential for misuse in generating misleading or incorrect information.
+* Cultural Nuance: Automated translation may miss critical cultural nuances, potentially leading to misinterpretations. Human oversight is recommended for sensitive or important translations.
+* Attribution: The training data is sourced from kambale/luganda-english-parallel-corpus. Please refer to the dataset card for its specific sourcing and licensing.
+
+# Future Work & Potential Improvements
+* Fine-tuning on domain-specific data.
+* Training with a larger parallel corpus if available.
+* Incorporating monolingual Luganda data through techniques like back-translation.
+* Experimenting with larger model architectures or pre-trained multilingual models as a base.
+* Implementing more sophisticated decoding strategies (e.g., beam search with length normalization).
+* Conducting a thorough human evaluation of translation quality.
 
 ## Disclaimer
-(Content similar to original)
+This model is provided "as-is" without warranty of any kind, express or implied. It was trained as part of an educational demonstration and may have limitations in accuracy, fluency, and robustness. Users should validate its suitability for their specific applications.