Instructions to use sucharush/MNLP_M3_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sucharush/MNLP_M3_document_encoder with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("sucharush/MNLP_M3_document_encoder")

sentences = [
    "How does a photocell control outdoor lighting?",
    "To solve this problem, we can use the binomial probability formula:\n\nP(X = k) = C(n, k) * p^k * (1-p)^(n-k)\n\nwhere:\n- P(X = k) is the probability of exactly k successes (faulty keyboards) in n trials (laptops produced)\n- C(n, k) is the number of combinations of n items taken k at a time (n! / (k!(n-k)!))\n- p is the probability of success (5% or 0.05)\n- n is the number of trials (400 laptops)\n- k is the number of successes (20 faulty keyboards)\n\nHowever, we want to find the probability of at least 20 faulty keyboards, so we need to find the sum of probabilities for k = 20, 21, 22, ..., 400.\n\nP(X >= 20) = 1 - P(X < 20) = 1 - Σ P(X = k) for k = 0 to 19\n\nNow, we can calculate the probabilities for each value of k and sum them up:\n\nP(X >= 20) = 1 - Σ C(400, k) * 0.05^k * 0.95^(400-k) for k = 0 to 19\n\nUsing a calculator or software to compute the sum, we get:\n\nP(X >= 20) ≈ 1 - 0.0184 = 0.9816\n\nSo, the probability that at least 20 laptops will have a faulty keyboard is approximately 98.16%.",
    "A photocell controls outdoor lighting by detecting the level of ambient light. It automatically turns the lights on when it becomes dark and off when it becomes light, functioning as a light-dependent switch for energy efficiency and convenience.",
    "Glycosylation with β-N-acetylglucosamine (O-GlcNAcylation) is one of the most complex post-translational modifications. The cycling of O-GlcNAc is controlled by two enzymes: UDP-NAc transferase (OGT) and O-GlcNAcase (OGA). We recently reported that endothelin-1 (ET-1) augments vascular levels of O-GlcNAcylated proteins. Here we tested the hypothesis that O-GlcNAcylation contributes to the vascular effects of ET-1 via activation of the RhoA/Rho-kinase pathway. Incubation of vascular smooth muscle cells (VSMCs) with ET-1 (0.1 μM) produces a time-dependent increase in O-GlcNAc levels. ET-1-induced O-GlcNAcylation is not observed when VSMCs are previously transfected with OGT siRNA, treated with ST045849 (OGT inhibitor) or atrasentan (ET(A) antagonist). ET-1 as well as PugNAc (OGA inhibitor) augmented contractions to phenylephrine in endothelium-denuded rat aortas, an effect that was abolished by the Rho kinase inhibitor Y-27632. Incubation of VSMCs with ET-1 increased expression of the phosphorylated forms of myosin phosphatase target subunit 1 (MYPT-1), protein kinase C-potentiated protein phosphatase 1 inhibitor protein (protein kinase C-potentiated phosphatase inhibitor-17), and myosin light chain (MLC) and RhoA expression and activity, and this effect was abolished by both OGT siRNA transfection or OGT inhibition and atrasentan. ET-1 also augmented expression of PDZ-Rho GEF (guanine nucleotide exchange factor) and p115-Rho GEF in VSMCs and this was prevented by OGT siRNA, ST045849, and atrasentan."
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Notebooks
Google Colab
Kaggle

MNLP_M3_document_encoder

File size: 1,463 Bytes

4efe6e2

{
  "added_tokens_decoder": {
    "0": {
      "content": "[PAD]",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    },
    "100": {
      "content": "[UNK]",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    },
    "101": {
      "content": "[CLS]",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    },
    "102": {
      "content": "[SEP]",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    },
    "103": {
      "content": "[MASK]",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    }
  },
  "clean_up_tokenization_spaces": true,
  "cls_token": "[CLS]",
  "do_basic_tokenize": true,
  "do_lower_case": true,
  "extra_special_tokens": {},
  "mask_token": "[MASK]",
  "max_length": 128,
  "model_max_length": 512,
  "never_split": null,
  "pad_to_multiple_of": null,
  "pad_token": "[PAD]",
  "pad_token_type_id": 0,
  "padding_side": "right",
  "sep_token": "[SEP]",
  "stride": 0,
  "strip_accents": null,
  "tokenize_chinese_chars": true,
  "tokenizer_class": "BertTokenizer",
  "truncation_side": "right",
  "truncation_strategy": "longest_first",
  "unk_token": "[UNK]"
}