Datasets:
char_count
string | cid
string | citations
string | court
string | decision_date
string | docket_number
string | first_page
string | head_matter
string | id
string | judges
string | jurisdiction
string | last_page
string | last_updated
string | name
string | name_abbreviation
string | parties
string | provenance
string | reporter
string | secondary_cid
string | text
string | volume
string | word_count
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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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