metadata-embedding
5000-podcast-conversations-with-metadata-and-embedding-dataset
🗂️ ReadyAI - 5,000 Podcast Conversations with Metadata and Embedding Dataset
ReadyAI, operating subnet 33 on the Bittensor Network is an open-source initiative focused on low-cost, resource-minimal pipelines for structuring raw data for AI applications.
This dataset is part of the ReadyAI Conversational Genome Project, leveraging the Bittensor decentralized network.
AI runs on structured data — and this dataset bridges the gap between raw conversation transcripts and structured… See the full description on the dataset page: https://huggingface.co/datasets/ReadyAi/5000-podcast-conversations-with-metadata-and-embedding-dataset.TalkPlayData-Extra-metadata-qwen3_embedding_0.6bcrossref_metadata_embeddings_split_2025Created vector embeddings for the abstract field for the dataset: bluuebunny/crossref_metadata_2025_split using mixedbread-ai/mxbai-embed-large-v1
crossref_metadata_embeddings_split_2025_binaryCreated vector embeddings for the abstract field for the dataset: bluuebunny/crossref_metadata_2025_split using mixedbread-ai/mxbai-embed-large-v1 and binarised it using:
# Function to binarise float embeddings
def binarise(row):
# Make it a numpy array, since batching sends it as list
float_vector = np.array(row['vector'], dtype=np.float32)
# Binarise
binary_vector = np.where(float_vector >= 0, 1, 0)
# Pack it to make it milvus compatible
row['vector'] =… See the full description on the dataset page: https://huggingface.co/datasets/bluuebunny/crossref_metadata_embeddings_split_2025_binary.dataset_cards_with_metadata_with_embeddingsmetadata_embedding