Instructions to use Lysandrec/MNLP_M2_document_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lysandrec/MNLP_M2_document_encoder with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Lysandrec/MNLP_M2_document_encoder")

sentences = [
    "Consider the set of points $S = \\{(x,y) : x \\text{ and } y \\text{ are non-negative integers } \\leq n\\}$. Find the number of squares that can be formed with vertices belonging to $S$ and sides parallel to the axes.",
    "<page_title> Waller Plan </page_title> <path> Waller_Plan > City plan </path> <section_title> City plan </section_title> <content> The plan also designated spaces for a hospital, an academy and university, churches, a courthouse and jail, an armory, and a penitentiary.With the surveying and grid plan completed, Waller and his associates drew up a plat dividing the city blocks into land lots. The first auction of lots was held on August 1, 1839, under a group of live oak trees in what was to be the city's southwestern public square; these trees have since been known as the \"Auction Oaks\". The auction raised $182,585 (equivalent to $5,018,000 in 2022), funds used to pay for the construction of government buildings for the new capital city. </content>",
    "<page_title> Heilbronn triangle problem </page_title> <path> Heilbronn_triangle_problem > Specific shapes and numbers </path> <section_title> Specific shapes and numbers </section_title> <content> Goldberg (1972) has investigated the optimal arrangements of n {\\displaystyle n} points in a square, for n {\\displaystyle n} up to 16. Goldberg's constructions for up to six points lie on the boundary of the square, and are placed to form an affine transformation of the vertices of a regular polygon. For larger values of n {\\displaystyle n} , Comellas & Yebra (2002) improved Goldberg's bounds, and for these values the solutions include points interior to the square. These constructions have been proven optimal for up to seven points. </content>",
    "<page_title> 14:9 aspect ratio </page_title> <path> 14:9_aspect_ratio > Mathematics </path> <section_title> Mathematics </section_title> <content> The aspect ratio of 14:9 (1.555...) is the arithmetic mean (average) of 16:9 and 4:3 (12:9), ( ( 16 / 9 ) + ( 12 / 9 ) ) ÷ 2 = 14 / 9 {\\displaystyle ((16/9)+(12/9))\\div 2=14/9} . More practically, it is approximately the geometric mean (the precise geometric mean is ( 16 / 9 ) × ( 4 / 3 ) ≈ 1.5396 ≈ 13.8: 9 {\\displaystyle {\\sqrt {(16/9)\\times (4/3)}}\\approx 1.5396\\approx 13.8:9} ), and in this sense is mathematically a compromise between these two aspect ratios: two equal area pictures (at 16:9 and 4:3) will intersect in a box with aspect ratio the geometric mean, as demonstrated in the image at top (14:9 is just slightly wider than the intersection). In this way 14:9 balances the needs of both 16:9 and 4:3, cropping or distorting both about equally. Similar considerations were used in the choice of 16:9 by the SMPTE, which balanced 2.35:1 and 4:3. </content>"
]
embeddings = model.encode(sentences)

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

Notebooks
Google Colab
Kaggle

MNLP_M2_document_encoder

File size: 1,464 Bytes

a571099

{
  "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": false,
  "cls_token": "[CLS]",
  "do_basic_tokenize": true,
  "do_lower_case": true,
  "extra_special_tokens": {},
  "mask_token": "[MASK]",
  "max_length": 128,
  "model_max_length": 384,
  "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]"
}