Enhance dataset card: Update task category and add sample usage

This pull request improves the dataset card for 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 on this dataset for downstream tasks like classification and retrieval, as highlighted in the paper abstract and the associated GitHub repository.
- Incorporating a comprehensive "Sample Usage" section with practical Python code snippets directly from the GitHub README. This section demonstrates how to install the necessary packages and use the mmBERT models for tasks such as generating multilingual embeddings, performing masked language modeling, and multilingual retrieval.

README.md

@@ -1,7 +1,7 @@
 ---
 license: mit
 task_categories:
-- fill-mask
+- feature-extraction
 tags:
 - pretraining
 - encoder
@@ -19,6 +19,98 @@ tags:
 
 This dataset is part of the complete, pre-shuffled training data used to train the [mmBERT encoder models](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4). Unlike the individual phase datasets, this version is ready for immediate use but **the mixture cannot be modified easily**. The data is provided in **decompressed MDS format** ready for use with [ModernBERT's Composer](https://github.com/mosaicml/composer) and the [ModernBERT training repository](https://github.com/answerdotai/ModernBERT).
 
+## Sample Usage
+
+The mmBERT models are available on the Hugging Face Hub and can be easily loaded using the `transformers` library. Here are some quick examples for feature extraction (getting embeddings), masked language modeling, and multilingual retrieval.
+
+First, install the necessary packages:
+```bash
+pip install torch>=1.9.0
+pip install transformers>=4.48.0
+```
+
+### Get Multilingual Embeddings (Feature Extraction)
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+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)
+
+print(f"Embeddings shape: {embeddings.shape}")
+```
+
+### Multilingual 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 for [MASK]: {predicted_words}")
+```
+
+### Multilingual Retrieval
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import numpy as np
+
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmbert-base")
+model = AutoModel.from_pretrained("jhu-clsp/mmbert-base")
+
+def get_embeddings(texts):
+    inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    # Mean pooling
+    embeddings = outputs.last_hidden_state.mean(dim=1)
+    return embeddings.numpy()
+
+# Multilingual document retrieval
+documents = [
+    "Artificial intelligence is transforming healthcare.",
+    "L'intelligence artificielle transforme les soins de santé.",
+    "人工智能正在改变医疗保健。",
+    "Climate change requires immediate action.",
+    "El cambio climático requiere acción inmediata."
+]
+
+query = "AI in medicine"
+
+# Get embeddings
+doc_embeddings = get_embeddings(documents)
+query_embedding = get_embeddings([query])
+
+# Compute similarities
+similarities = np.dot(doc_embeddings, query_embedding.T).flatten()
+ranked_docs = np.argsort(similarities)[::-1]
+
+print("Most similar documents:")
+for i, doc_idx in enumerate(ranked_docs[:3]):
+    print(f"{i+1}. {documents[doc_idx]} (score: {similarities[doc_idx]:.3f})")
+```
+
 ## Licensing & Attribution
 
 This dataset aggregates multiple open-source datasets under permissive licenses. See individual source datasets for specific attribution requirements.
@@ -35,12 +127,12 @@ This dataset aggregates multiple open-source datasets under permissive licenses.
 
 ```bibtex
 @misc{marone2025mmbertmodernmultilingualencoder,
-      title={mmBERT: A Modern Multilingual Encoder with Annealed Language Learning}, 
+      title={mmBERT: A Modern Multilingual Encoder with Annealed Language Learning},
       author={Marc Marone and Orion Weller and William Fleshman and Eugene Yang and Dawn Lawrie and Benjamin Van Durme},
       year={2025},
       eprint={2509.06888},
       archivePrefix={arXiv},
       primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2509.06888}, 
+      url={https://arxiv.org/abs/2509.06888},
 }
 ```