Update README.md

README.md

@@ -1,11 +1,120 @@
 ---
-license: mit
-datasets:
-- Helsinki-NLP/europarl
+license: apache-2.0
 language:
-- de
-- en
+  - en
+  - de
 tags:
-- LOCALIZATION
-- NMT
----
+  - translation
+  - marian
+  - nmt
+  - encoder-decoder
+pipeline_tag: translation
+widget:
+  - text: "The weather is beautiful today."
+    example_title: "Simple sentence"
+  - text: "Machine learning is transforming the way we build software applications."
+    example_title: "Technical text"
+  - text: "The European Union has proposed new regulations on artificial intelligence."
+    example_title: "Formal text"
+datasets:
+  - opus100
+  - europarl_bilingual
+  - un_pc
+model-index:
+  - name: pumadic-en-de
+    results: []
+---
+
+# Pumatic English-German Translation Model
+
+A neural machine translation model for English to German translation built with the MarianMT architecture.
+
+## Model Description
+
+- **Model type:** Encoder-Decoder (MarianMT architecture)
+- **Language pair:** English → German
+- **Parameters:** ~74.7M
+- **GPU:** H100
+- **Trained by:** [pumad](https://huggingface.co/pumad)
+
+## Training Details
+
+### Training Data
+
+The model was trained on high-quality parallel corpora:
+- **OPUS-100** - Multilingual parallel corpus
+- **Europarl** - European Parliament proceedings
+- **UN Parallel Corpus (UNPC)** - United Nations documents
+
+### Training Procedure
+
+- **Hardware:** NVIDIA H100 GPU
+- **Framework:** Hugging Face Transformers
+- **Batch size:** 128
+- **Learning rate:** 2e-5
+- **Epochs:** 1 (fine-tuning)
+- **Max sequence length:** 128 tokens
+
+### Data Preprocessing
+
+- Quality filtering: Removed pairs with fewer than 5 words or more than 200 words
+- Length ratio filtering: Excluded pairs with extreme length ratios (< 0.5 or > 2.0)
+- Deduplication: Removed duplicate source sentences
+
+## Usage
+
+### Using the Transformers library
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+model_name = "pumad/pumadic-en-de"
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+model = MarianMTModel.from_pretrained(model_name)
+
+text = "Hello, how are you today?"
+inputs = tokenizer(text, return_tensors="pt", padding=True)
+translated = model.generate(**inputs)
+output = tokenizer.decode(translated[0], skip_special_tokens=True)
+print(output)
+```
+
+### Using the Pipeline API
+
+```python
+from transformers import pipeline
+
+translator = pipeline("translation", model="pumad/pumadic-en-de")
+result = translator("The quick brown fox jumps over the lazy dog.")
+print(result[0]['translation_text'])
+```
+
+## Demo
+
+Try this model live at [pumatic.eu](https://pumatic.eu)
+
+API documentation available at [pumatic.eu/docs](https://pumatic.eu/docs)
+
+## Limitations
+
+- Optimized for general-purpose translation; domain-specific terminology may vary in quality
+- Maximum input length of ~400 characters per chunk for optimal results
+- Best performance on formal/written text; colloquial expressions may be less accurate
+
+## License
+
+Apache 2.0
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{pumatic-en-de,
+  author = {pumad},
+  title = {Pumatic English-German Translation Model},
+  year = {2025},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/pumad/pumadic-en-de}
+}
+```