Create README.md

README.md

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+---
+datasets:
+- mnaguib/WikiNER
+pipeline_tag: token-classification
+---
+# Fine-Tuning mDeBERTa for Named Entity Recognition (NER)
+
+## 📌 Model Overview
+
+This repository contains a fine-tuned version of `MoritzLaurer/mDeBERTa-v3-base-mnli-xnli` for **Named Entity Recognition (NER)** using the `mnaguib/WikiNER` dataset in multiple languages.
+
+## 🚀 Features
+
+- **Pretrained on mDeBERTa**: A powerful multilingual model for text understanding.
+- **Fine-tuned for NER**: Detects entities such as persons (`PER`), locations (`LOC`), organizations (`ORG`), and more.
+
+## 📖 Training Details
+
+- **Base model**: `MoritzLaurer/mDeBERTa-v3-base-mnli-xnli`
+- **Dataset**: `mnaguib/WikiNER`
+- **Languages**:  English (`en`), Spanish (es), ...
+- **Epochs**: `2`
+- **Optimizer**: AdamW
+- **Loss function**: CrossEntropyLoss
+
+## Inference Example
+
+To use the model for inference:
+
+```python
+import torch
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+# Load the model and tokenizer
+model_path = "jordigonzm/mdeberta-v3-base-multilingual-ner"
+model = AutoModelForTokenClassification.from_pretrained(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model.eval()
+
+# NER Prediction Function
+def predict_ner(text):
+    tokens = tokenizer(text, truncation=True, padding=True, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**tokens)
+    logits = outputs.logits
+    predictions = torch.argmax(logits, dim=-1).squeeze().tolist()
+    tokens_decoded = tokenizer.convert_ids_to_tokens(tokens["input_ids"].squeeze().tolist())
+    return list(zip(tokens_decoded, predictions))
+
+# Example
+text = "text = "The Mona Lisa is located in the Louvre Museum, in Paris."
+result = predict_ner(text)
+print(result)
+```
+
+## Model Usage
+
+You can load the model directly from Hugging Face:
+
+```python
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+model = AutoModelForTokenClassification.from_pretrained("jordigonzm/mdeberta-v3-base-multilingual-ner")
+tokenizer = AutoTokenizer.from_pretrained("jordigonzm/mdeberta-v3-base-multilingual-ner")
+```
+
+---