Update metadata (language, task categories) and add model usage examples

README.md

@@ -1,9 +1,9 @@
 ---
 language:
-- en
+- mul
 license: mit
 task_categories:
-- fill-mask
+- feature-extraction
 tags:
 - pretraining
 - language-modeling
@@ -69,6 +69,50 @@ for sample in dataset:
     # Process your data...
 ```
 
+## 💡 Sample Usage of mmBERT Models
+
+Models trained on this pre-training data can be used for various multilingual tasks. The following examples demonstrate how to get started with `mmBERT` models.
+
+### Installation
+```bash
+pip install torch>=1.9.0
+pip install transformers>=4.48.0
+```
+
+### Small Model for Fast Inference (Feature Extraction)
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmbert-small")
+model = AutoModel.from_pretrained("jhu-clsp/mmbert-small")
+
+# Example: Get multilingual embeddings
+inputs = tokenizer("Hello world! 你好世界! Bonjour le monde!", return_tensors="pt")
+outputs = model(**inputs)
+embeddings = outputs.last_hidden_state.mean(dim=1)
+```
+
+### Base Model for Masked Language Modeling
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmbert-base")
+model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/mmbert-base")
+
+# Example: Multilingual masked language modeling
+text = "The capital of [MASK] is Paris."
+inputs = tokenizer(text, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Get predictions for [MASK] tokens
+mask_indices = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)
+predictions = outputs.logits[mask_indices]
+top_tokens = torch.topk(predictions, 5, dim=-1)
+predicted_words = [tokenizer.decode(token) for token in top_tokens.indices[0]]
+print(f"Predictions: {predicted_words}")
+```
 
 ## 🔗 Related Resources