Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:714
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use jet-taekyo/mpnet_finetuned_recursive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jet-taekyo/mpnet_finetuned_recursive with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jet-taekyo/mpnet_finetuned_recursive") sentences = [ "What does the term 'rights, opportunities, or access' encompass in this framework?", "10 \nGAI systems can ease the unintentional production or dissemination of false, inaccurate, or misleading \ncontent (misinformation) at scale, particularly if the content stems from confabulations. \nGAI systems can also ease the deliberate production or dissemination of false or misleading information \n(disinformation) at scale, where an actor has the explicit intent to deceive or cause harm to others. Even \nvery subtle changes to text or images can manipulate human and machine perception. \nSimilarly, GAI systems could enable a higher degree of sophistication for malicious actors to produce \ndisinformation that is targeted towards specific demographics. Current and emerging multimodal models \nmake it possible to generate both text-based disinformation and highly realistic “deepfakes” – that is, \nsynthetic audiovisual content and photorealistic images.12 Additional disinformation threats could be \nenabled by future GAI models trained on new data modalities.", "74. See, e.g., Heather Morrison. Virtual Testing Puts Disabled Students at a Disadvantage. Government\nTechnology. May 24, 2022.\nhttps://www.govtech.com/education/k-12/virtual-testing-puts-disabled-students-at-a-disadvantage;\nLydia X. Z. Brown, Ridhi Shetty, Matt Scherer, and Andrew Crawford. Ableism And Disability\nDiscrimination In New Surveillance Technologies: How new surveillance technologies in education,\npolicing, health care, and the workplace disproportionately harm disabled people. Center for Democracy\nand Technology Report. May 24, 2022.\nhttps://cdt.org/insights/ableism-and-disability-discrimination-in-new-surveillance-technologies-how\nnew-surveillance-technologies-in-education-policing-health-care-and-the-workplace\ndisproportionately-harm-disabled-people/\n69", "persons, Asian Americans and Pacific Islanders and other persons of color; members of religious minorities; \nwomen, girls, and non-binary people; lesbian, gay, bisexual, transgender, queer, and intersex (LGBTQI+) \npersons; older adults; persons with disabilities; persons who live in rural areas; and persons otherwise adversely \naffected by persistent poverty or inequality. \nRIGHTS, OPPORTUNITIES, OR ACCESS: “Rights, opportunities, or access” is used to indicate the scoping \nof this framework. It describes the set of: civil rights, civil liberties, and privacy, including freedom of speech, \nvoting, and protections from discrimination, excessive punishment, unlawful surveillance, and violations of \nprivacy and other freedoms in both public and private sector contexts; equal opportunities, including equitable \naccess to education, housing, credit, employment, and other programs; or, access to critical resources or" ] 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!