HuggingMapper tutorial

# uncomment if colab
#!pip install pandas hugging-mapper

Getting Embeddings.

Start by importing HuggingMapper

from hugger.mapper import HuggingMapper

Initializing HuggingMapper will load the given huggingface model

# init
mapper = HuggingMapper(
    model_name="sentence-transformers/all-MiniLM-L6-v2",
)

Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
BertModel LOAD REPORT from: sentence-transformers/all-MiniLM-L6-v2
Key                     | Status     |  | 
------------------------+------------+--+-
embeddings.position_ids | UNEXPECTED |  | 

Notes:
- UNEXPECTED:	can be ignored when loading from different task/architecture; not ok if you expect identical arch.

Get embeddings for given text

# generate embedding for a single text
embedding = mapper.embed_text("Good morning")
print(embedding.shape)

# generate embeddings for a list of texts
embeddings = mapper.embed_text(["Hello world", "Good evening", "Lunch time!"])
print(embeddings.shape)

torch.Size([1, 384])
torch.Size([3, 384])