Create README.md

README.md

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+---
+language:
+- eo
+- en
+- es
+- ca
+tags:
+- translation
+- machine-translation
+- marian
+- opus-mt
+- multilingual
+license: cc-by-4.0
+pipeline_tag: translation
+metrics:
+- bleu
+- chrf
+---
+
+# Catalan, English, Spanish -> Esperanto MT Model
+
+## Model description
+
+This repository contains a **multilingual MarianMT** model for **(English, Spanish, Catalan) → Esperanto** translation.
+
+## Usage
+
+The model is loaded and used with `transformers` as:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+import torch
+
+model_name = "Helsinki-NLP/opus-mt-caenes-eo"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = MarianMTModel.from_pretrained(model_name).to(device)
+tokenizer = MarianTokenizer.from_pretrained(model_name)
+
+source_texts = [
+	"Buenos días, qué tal?",
+	"Bon dia, com estàs?",
+	"Good morning, how are you?"
+	]
+
+inputs = tokenizer(source_texts, return_tensors="pt", padding=True, truncation=True)
+inputs = {k: v.to(device) for k, v in inputs.items()}
+
+translated_ids = model.generate(inputs["input_ids"])
+translated_texts = tokenizer.batch_decode(translated_ids, skip_special_tokens=True)
+
+for src, tgt in zip(source_texts, translated_texts):
+    print(f"Source: {src} => Translated: {tgt}")
+````
+
+## Training data
+
+The model was trained using **Tatoeba** parallel data, with **FLORES-200** used as the development set.
+
+Training sentence-pair counts:
+
+* **ca-eo**: 672,931
+* **es-eo**: 4,677,945
+* **eo-en**: 5,000,000
+
+## Evaluation on FLORES
+
+| Language Pair |  BLEU |  ChrF++ |
+| ------------- | ----: | ----: |
+| spa-epo       | 16.25 | 49.10 |
+| cat-epo       | 21.43 | 51.37 |
+| eng-epo       | 26.42 | 58.23 |