cryptoNER

Identifies and classifies cryptocurrency-specific named entities (wallet addresses, token names, exchange names, contract addresses) across 100+ languages using XLM-RoBERTa's multilingual transformer backbone. The model performs token-level classification by fine-tuning FacebookAI/xlm-roberta-base on cryptocurrency domain data, enabling it to recognize crypto entities even in non-English text through shared cross-lingual embeddings learned during pre-training.

Performs token-level sequence labeling by leveraging XLM-RoBERTa's shared multilingual embedding space, where tokens from different languages map to semantically similar positions in a 768-dimensional vector space. The model classifies each token independently using a linear classification head on top of contextualized embeddings, enabling zero-shot transfer to unseen languages through the shared embedding geometry learned during XLM-RoBERTa's pre-training on 100+ languages.

Applies domain-specific fine-tuning to XLM-RoBERTa's pre-trained transformer backbone using supervised learning on cryptocurrency-annotated text. The model generates contextualized token embeddings (where each token's representation depends on surrounding context) and passes them through a linear classification layer to predict entity labels. Fine-tuning updates all transformer weights via backpropagation on the cryptocurrency NER task, adapting the general-purpose language model to recognize crypto-specific patterns.

Processes multiple documents simultaneously through the model using HuggingFace's pipeline abstraction, which handles tokenization, padding, batching, and output decoding automatically. The pipeline manages variable-length inputs by padding shorter sequences and truncating longer ones to a maximum length, then aggregates predictions across the batch for efficient GPU utilization. Output is automatically decoded from token-level labels back to human-readable entity spans with character offsets.

Converts token-level classification predictions back to entity spans in the original text by tracking character offsets through the tokenization process. The model maintains a mapping between token indices and their positions in the original text, allowing it to reconstruct entity boundaries (start and end character positions) from token-level labels. This enables downstream systems to directly reference entities in the source text without manual span reconstruction.