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)