Improve dataset card: Update task category and add sample usage

This pull request improves the dataset card for the mmBERT Training Data by:
- Updating the `task_categories` metadata from `fill-mask` to `feature-extraction`. This change better reflects the primary utility of models trained with this dataset, which are designed for downstream tasks like classification and retrieval that rely on extracted features/embeddings.
- Adding a "Sample Usage" section, including installation steps and code snippets for fast inference (multilingual embeddings) and masked language modeling, directly from the associated GitHub repository. This helps users quickly understand how to leverage models trained with this dataset.

README.md

@@ -1,7 +1,7 @@
 ---
 license: mit
 task_categories:
-- fill-mask
+- feature-extraction
 tags:
 - pretraining
 - encoder
@@ -23,6 +23,53 @@ This dataset is part of the complete, pre-shuffled training data used to train t
 
 This dataset aggregates multiple open-source datasets under permissive licenses. See individual source datasets for specific attribution requirements.
 
+## Sample Usage
+
+The mmBERT models trained with this dataset can be easily loaded and used for various tasks, including getting multilingual embeddings and masked language modeling.
+
+### Installation
+```bash
+pip install torch>=1.9.0
+pip install transformers>=4.48.0
+```
+
+### 30-Second Examples
+
+**Small Model for Fast Inference:**
+```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 Classification:**
+```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
 
 - **Models**: [mmBERT Model Suite](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4)