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metadata
language:
  - en
pipeline_tag: token-classification
widget:
  - text: >-
      Let P be a G-poset. The strong compatibility graph of P, denoted by C_P,
      is the graph C_P with vertex set P, and two elements x, y∈ P are adjacent
      if there is an element g∈ G∖{e} such that x and g· y are comparable in P
      and y∉ [x], where [x]={g· x : g∈ G}.
    example_title: Strong compatibility graph
  - text: >-
      A simplicial map f : X → Y between simplicial complexes X and Y is a map
      which sends vertices to vertices, and whenever vertices v_0, ..., v_k∈ X
      span a simplex σ of X then their images span a simplex τ of Y and we have
      f(σ) = τ. Therefore a simplicial map is determined by its values on the
      vertex set of X. A simplicial map is nondegenerate if it is injective on
      each simplex.
    example_title: simplicial map; nondegenerate
  - text: >-
      A vertical strip is a skew shape (either partition or composition) whose
      diagram contains at most one cell per row. A horizontal strip is a skew
      shape whose diagram contains at most one cell per column.
    example_title: vertical strip; horizontal strip
  - text: >-
      Permutations ω and π are C-equivalent , denoted ωπ, if ωQ∼π  and
      (P(ω))=(P(π)).
       We denote the C-equivalence class of the permutation π by [π]_C. 
       The rectified shape of [π]_C is (P(π)). 
       Two SCT T and T'  are C-equivalent T T' if they have the same skew shape and w_col(T) w_col(T'). 
       We denote the C-equivalence class of T by [T]_C. 
       The rectified shape of [T]_C is (T). 
       We say that [T]_C is complete if 
       { w_col(T') : T'∈ [T]_C } = [w_col(T)]_C.
    example_title: C-equivalent; rectified shape; complete
  - text: A Young graph Y such that Y≃ Y(10, 9) is called a 1089 graph.
    example_title: 1089 graph
  - text: >-
      A subset 𝒢 is a Gröbner basis for I if the leading term of each member of
      I is divided by the leading term of a member of 𝒢. That is, 𝒢 is a
      Gröbner basis if in_≺(I)=LT_≺(g):g∈𝒢.
    example_title: Gröbner basis

How to use

You can use this model directly with a pipeline for token classification. Given a mathematical definition from the text of an academic article, the model shall identify the term(s) defined with it.

from transformers import pipeline
# Here, we use the prediction of the first word piece of every word.
pipe = pipeline(model="InriaValda/roberta-base_definiendum",
                aggregation_strategy="first") 
pipe("A Young graph Y such that Y≃ Y(10, 9) is called a 1089 graph.")

Our Paper

Extracting Definienda in Mathematical Scholarly Articles with Transformers (Accepted by 2nd WIESP @ IJCNLP-AACL 2023)

An 8 minutes' video about our work

https://youtu.be/tUioJooDDio?si=6EnTN_5l-9t86IKk