char_count
stringclasses 1
value | cid
stringlengths 0
59
| citations
stringclasses 1
value | court
stringclasses 1
value | decision_date
stringclasses 1
value | docket_number
stringclasses 1
value | first_page
stringclasses 1
value | head_matter
stringclasses 1
value | id
stringclasses 1
value | judges
stringclasses 1
value | jurisdiction
stringclasses 1
value | last_page
stringclasses 1
value | last_updated
stringclasses 1
value | name
stringclasses 1
value | name_abbreviation
stringclasses 1
value | parties
stringclasses 1
value | provenance
stringclasses 1
value | reporter
stringclasses 1
value | secondary_cid
stringclasses 1
value | text
stringclasses 1
value | volume
stringclasses 1
value | word_count
stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Original Repository:
https://huggingface.co/datasets/justicedao/Caselaw_Access_Project_embeddings/
This is an embeddings dataset for the Caselaw Access Project, created by a user named Endomorphosis.
Each caselaw entry is hashed with IPFS / multiformats, so retrieval of the document can be made over the IPFS / filecoin network
The ipfs content id "cid" is the primary key that links the dataset to the embeddings, should you want to retrieve from the dataset instead.
The dataset has been had embeddings generated with three models: thenlper/gte-small, Alibaba-NLP/gte-large-en-v1.5, and Alibaba-NLP/gte-Qwen2-1.5B-instruct
Those models have a context length of 512, 8192, and 32k tokens respectively, with 384, 1024, and 1536 dimensions
These embeddings are put into 4096 clusters, the centroids for each cluster is provided, as well as the content ids for each cluster, for each model.
To search the embeddings on the client side, it would be wise to first query against the centroids, and then retrieve the closest gte-small cluster, and then query against the cluster.
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