Update task category and add sample usage

This PR updates the `task_categories` to `feature-extraction` to better reflect the primary application of models trained with this dataset. It also adds sample usage snippets for the `mmBERT` models, demonstrating how users can leverage models trained on this data for feature extraction and classification tasks, as found in the associated GitHub repository.

README.md

@@ -1,11 +1,12 @@
 ---
 license: mit
 task_categories:
-- fill-mask
+- feature-extraction
 tags:
 - pretraining
 - encoder
 - multilingual
+- fill-mask
 ---
 
 # mmBERT Training Data (Ready-to-Use)
@@ -19,6 +20,45 @@ 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 (of models trained with this data)
+
+Here are a few quick examples showing how to use the models trained with this dataset for various tasks.
+
+### 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 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}")
+```
+
 ## Licensing & Attribution
 
 This dataset aggregates multiple open-source datasets under permissive licenses. See individual source datasets for specific attribution requirements.