Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:34235
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use zacbrld/MNLP_M3_document_encoder_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use zacbrld/MNLP_M3_document_encoder_V1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zacbrld/MNLP_M3_document_encoder_V1") sentences = [ "What is This?", "Bellcranks are also seen in automotive applications, such as in the linkage connecting the throttle pedal to the carburetor or connecting the brake pedal to the master cylinder In vehicle suspensions, bellcranks are used in pullrod and pushrod suspensions in cars or in the Christie suspension in tanks More vertical suspension designs such as MacPherson struts may not be feasible in some vehicle designs due to space, aerodynamic, or other design constraints; bellcranks translate the vertical motion of the wheel into horizontal motion, allowing the suspension to be mounted transversely or longitudinally within the vehicle", "DynaMo was also used as the face of the BBC's parental assistance website This was created for parents to assist children with homework There was also a section called \"DynaMo's Den\" which included educational games for children The website was activated on 2 October 1998", "The diode equation above is an example of an element constitutive equation of the general form,\n\n \n \n \n f\n (\n v\n ,\n i\n )\n =\n 0\n \n \n {\\displaystyle f(v,i)=0}\n \n\nThis can be thought of as a non-linear resistor The corresponding constitutive equations for non-linear inductors and capacitors are respectively;\n\n \n \n \n f\n (\n v\n ,\n φ\n )\n =\n 0\n \n \n {\\displaystyle f(v,\\varphi )=0}\n \n\n \n \n \n f\n (\n v\n ,\n q\n )\n =\n 0\n \n \n {\\displaystyle f(v,q)=0}\n \n\nwhere f is any arbitrary function, φ is the stored magnetic flux and q is the stored charge" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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