Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:3430
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use jeevanions/finetuned_arctic-embedd-l with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jeevanions/finetuned_arctic-embedd-l with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jeevanions/finetuned_arctic-embedd-l") sentences = [ "What are some illustrative cases that show the implementation of the AI Bill of Rights?", "SECTION TITLE\nAPPENDIX\nListening to the American People \nThe White House Office of Science and Technology Policy (OSTP) led a yearlong process to seek and distill \ninput from people across the country – from impacted communities to industry stakeholders to \ntechnology developers to other experts across fields and sectors, as well as policymakers across the Federal \ngovernment – on the issue of algorithmic and data-driven harms and potential remedies. Through panel \ndiscussions, public listening sessions, private meetings, a formal request for information, and input to a \npublicly accessible and widely-publicized email address, people across the United States spoke up about \nboth the promises and potential harms of these technologies, and played a central role in shaping the \nBlueprint for an AI Bill of Rights. \nPanel Discussions to Inform the Blueprint for An AI Bill of Rights \nOSTP co-hosted a series of six panel discussions in collaboration with the Center for American Progress,", "existing human performance considered as a performance baseline for the algorithm to meet pre-deployment, \nand as a lifecycle minimum performance standard. Decision possibilities resulting from performance testing \nshould include the possibility of not deploying the system. \nRisk identification and mitigation. Before deployment, and in a proactive and ongoing manner, poten\ntial risks of the automated system should be identified and mitigated. Identified risks should focus on the \npotential for meaningful impact on people’s rights, opportunities, or access and include those to impacted \ncommunities that may not be direct users of the automated system, risks resulting from purposeful misuse of \nthe system, and other concerns identified via the consultation process. Assessment and, where possible, mea\nsurement of the impact of risks should be included and balanced such that high impact risks receive attention", "confidence that their rights, opportunities, and access as well as their expectations about technologies are respected. \n3\nHOW THESE PRINCIPLES CAN MOVE INTO PRACTICE: \nThis section provides real-life examples of how these guiding principles can become reality, through laws, policies, and practices. \nIt describes practical technical and sociotechnical approaches to protecting rights, opportunities, and access. \nThe examples provided are not critiques or endorsements, but rather are offered as illustrative cases to help \nprovide a concrete vision for actualizing the Blueprint for an AI Bill of Rights. Effectively implementing these \nprocesses require the cooperation of and collaboration among industry, civil society, researchers, policymakers, \ntechnologists, and the public. \n14" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!