Improve dataset card: update task categories, add relevant tags, language, and sample usage

This PR updates the dataset card for the mmBERT training data to better reflect its primary use cases and improve discoverability.

Key changes include:
- **Metadata Update**:
- `task_categories`: Added `feature-extraction` to highlight the primary utility of models trained with this data for generating embeddings for downstream tasks. `fill-mask` is retained as it represents the pre-training objective.
- `language`: Added `mul` (multilingual) to accurately reflect the dataset's extensive language coverage (over 1800 languages) and improve discoverability.
- `tags`: Added `text-classification` and `text-retrieval` to further emphasize common applications of models trained on this dataset.
- **Sample Usage Section**: Added a comprehensive "Sample Usage" section with code snippets directly from the associated GitHub repository's README. These examples demonstrate how to install dependencies and use mmBERT models for tasks such as:
- Generating multilingual embeddings (feature extraction)
- Masked language modeling
- Multilingual retrieval
- Classification

These updates make the dataset card more informative and user-friendly for researchers and developers.

README.md

@@ -1,11 +1,16 @@
 ---
 license: mit
 task_categories:
+- feature-extraction
 - fill-mask
+language:
+- mul
 tags:
 - pretraining
 - encoder
 - multilingual
+- text-classification
+- text-retrieval
 ---
 
 # mmBERT Training Data (Ready-to-Use)
@@ -19,6 +24,129 @@ 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
+
+Models trained on this dataset can be used for various tasks, including generating multilingual embeddings, masked language modeling, classification, and multilingual retrieval.
+
+### Installation
+```bash
+pip install torch>=1.9.0
+pip install transformers>=4.48.0
+```
+
+### Small Model for Fast Inference (Multilingual Embeddings)
+```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}")
+```
+
+### Classification Task
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch.nn as nn
+import torch
+
+# Load model for classification
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmbert-base")
+encoder = AutoModel.from_pretrained("jhu-clsp/mmbert-base")
+
+# Add classification head
+class MultilingualClassifier(nn.Module):
+    def __init__(self, encoder, num_classes):
+        super().__init__()
+        self.encoder = encoder
+        self.classifier = nn.Linear(encoder.config.hidden_size, num_classes)
+        self.dropout = nn.Dropout(0.1)
+    
+    def forward(self, input_ids, attention_mask=None):
+        outputs = self.encoder(input_ids, attention_mask=attention_mask)
+        pooled_output = outputs.last_hidden_state[:, 0]  # Use [CLS] token
+        pooled_output = self.dropout(pooled_output)
+        return self.classifier(pooled_output)
+
+# Initialize classifier
+model = MultilingualClassifier(encoder, num_classes=3)
+
+# Example multilingual inputs
+texts = [
+    "This is a positive review.",
+    "Ceci est un avis négatif.",
+    "这是一个中性评价。"
+]
+inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
+predictions = model(**inputs)
+```
+
+### 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.