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README.md

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+# barbaroo/nllb_200_600M_en_fo
+
+## Model Description
+
+- **Model Architecture**: This model is based on the [NLLB 600M architecture](https://huggingface.co/facebook/nllb-200-distilled-600M) and weights.  
+- **Languages**: This checkpoint is fine-tuned to translate from **English** (`en`) to **Faroese** (`fo`).  
+- **Size**: ~600M parameters.  
+- **Finetuning Datasets**: 
+  - [Sprotin_parallel](https://huggingface.co/datasets/barbaroo/Sprotin_parallel)  
+  - [fo_en_synthetic](https://huggingface.co/datasets/barbaroo/fo_en_synthetic)
+  - **Training Regime**: Trained until convergence (about 2 epochs).  
+- **License**: Inherits the original licenses of the [NLLB 600M model](https://huggingface.co/facebook/nllb-200-distilled-600M).  
+
+## Intended Use
+
+- **Primary Use Case**: Translate text from English to Faroese.
+- **Audience**: Researchers, developers, or anyone interested in Faroese language processing.
+- **Usage Scenarios**:
+  - Building Faroese-English translation tools  
+  - Language research and corpus analysis
+  - Synthetic data creation 
+
+> **Important**: While the model can produce fluent translations, it is not guaranteed to be perfectly accurate on all inputs. Users should verify critical or sensitive content through human experts.
+
+## How to Use
+
+Below is a simple usage example in Python with [Hugging Face Transformers](https://github.com/huggingface/transformers):
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+model_name = "barbaroo/nllb_200_600M_en_fo"
+translator = pipeline("translation", model=model_name, tokenizer=model_name)
+
+text = "Hello, how are you?"
+translation = translator(text)
+print(translation)