README.md

metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:100000
  - loss:TripletLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
  - source_sentence: >-
      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.
    sentences:
      - >-
        <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>
  - source_sentence: >-
      Solve the equation \(7k^2 + 9k + 3 = d \cdot 7^a\) for positive integers
      \(k\), \(d\), and \(a\), where \(d < 7\).
    sentences:
      - >-
        <page_title> Rational square </page_title> <path> Square_numbers >
        Properties </path> <section_title> Properties </section_title> <content>
        Three squares are not sufficient for numbers of the form 4k(8m + 7). A
        positive integer can be represented as a sum of two squares precisely if
        its prime factorization contains no odd powers of primes of the form 4k
        + 3. This is generalized by Waring's problem. </content>
      - >-
        <page_title> Personally Controlled Electronic Health Record
        </page_title> <path> Personally_Controlled_Electronic_Health_Record >
        Registration > Healthcare Identifiers Service (HI Service) </path>
        <section_title> Healthcare Identifiers Service (HI Service)
        </section_title> <content> The Healthcare Identifiers Service (HI
        Service) was established by the federal, state and territory governments
        to create unique identifiers for healthcare providers and individuals
        seeking healthcare. It was designed and implemented by Medicare
        Australia under the control of the NEHTA. The HI Service allocates three
        types of Healthcare Identifiers: Individual healthcare identifier (i.e.,
        who received the service) The Individual Healthcare Identifier (IHI) is
        a unique 16 digit reference number that is used to identify individuals
        within the healthcare system. The healthcare provider can retrieve a
        registered patients IHI via the Healthcare Identifier Service by
        entering in the correct name, DOB, and Medicare number which will
        automatically retrieve the patients unique IHI from the system.
        </content>
      - >-
        <page_title> Systematic sampling </page_title> <path>
        Systematic_sampling </path> <section_title> Summary </section_title>
        <content> We want to give unit A a 20% probability of selection, unit B
        a 40% probability, and so on up to unit E (100%). Assuming we maintain
        alphabetical order, we allocate each unit to the following interval: A:
        0 to 0.2 B: 0.2 to 0.6 (= 0.2 + 0.4) C: 0.6 to 1.2 (= 0.6 + 0.6) D: 1.2
        to 2.0 (= 1.2 + 0.8) E: 2.0 to 3.0 (= 2.0 + 1.0) If our random start was
        0.156, we would first select the unit whose interval contains this
        number (i.e. A). Next, we would select the interval containing 1.156
        (element C), then 2.156 (element E). If instead our random start was
        0.350, we would select from points 0.350 (B), 1.350 (D), and 2.350 (E).
        </content>
  - source_sentence: >-
      Given the linear transformation \( T: \mathbb{R}^3 \rightarrow
      \mathbb{R}^3 \) defined by \( T(x) = A(x) \) where \( A = \begin{pmatrix}
      1 & 1 & 1 \\ 0 & 1 & 2 \\ 1 & 2 & 2 \end{pmatrix} \), find the inverse
      transformation \( T^{-1}(x) \).
    sentences:
      - >-
        <page_title> Genome sequence </page_title> <path> Genomic_sequence >
        Eukaryotic genomes </path> <section_title> Eukaryotic genomes
        </section_title> <content> In addition to the chromosomes in the
        nucleus, organelles such as the chloroplasts and mitochondria have their
        own DNA. Mitochondria are sometimes said to have their own genome often
        referred to as the "mitochondrial genome". The DNA found within the
        chloroplast may be referred to as the "plastome". </content>
      - >-
        <page_title> Right realism </page_title> <path> Right_realism > Overview
        > Rational choice theory </path> <section_title> Rational choice theory
        </section_title> <content> For example, in 1960 the steering columns of
        all cars in Germany were equipped with locks and the result was a 60 per
        cent reduction in car thefts. Whereas, in Great Britain only new cars
        were so equipped with the result being crime was displaced to the older
        unequipped cars. However, no evidence exists to suggest that an obscene
        phone caller will begin a career as a burglar. In response, Akers (1990)
        says that rational choice theorists make so many exceptions to the pure
        rationality stressed in their own models that nothing sets them apart
        from other theorists. Further, the rational choice models in literature
        have various situational or cognitive constraints and deterministic
        notions of cause and effect that render them, "...indistinguishable from
        current 'etiological' or 'positivist' theories." </content>
      - >-
        <page_title> Elementary row operations </page_title> <path>
        Row_operations > Elementary row operations > Row-switching
        transformations > Properties </path> <section_title> Properties
        </section_title> <content> The inverse of this matrix is itself: T i , j
        − 1 = T i , j . {\displaystyle T_{i,j}^{-1}=T_{i,j}.} Since the
        determinant of the identity matrix is unity, det ( T i , j ) = − 1.
        {\displaystyle \det(T_{i,j})=-1.} </content>
  - source_sentence: |-
      If |x - 5| = 23 what is the sum of all the values of x.
      A. A)46
      B. B)10
      C. C)56
      D. D)-46
      E. E)28
    sentences:
      - >-
        <page_title> BMX racing </page_title> <path> BMX_racing > General rules
        of advancement in organized BMX racing > Professionals </path>
        <section_title> Professionals </section_title> <content> For example, if
        a rider participates in 13 national events, their best 10 will be
        considered and their worst three disregarded. This qualification must be
        met on the national level to wear National numbers one through ten on
        the number plate the following year. </content>
      - >-
        <page_title> Construction of the real numbers </page_title> <path>
        Constructions_of_real_numbers > Axiomatic definitions > Axioms > On
        models </path> <section_title> On models </section_title> <content> f(x
        +ℝ y) = f(x) +S f(y) and f(x ×ℝ y) = f(x) ×S f(y), for all x and y in R
        . {\displaystyle \mathbb {R} .} x ≤ℝ y if and only if f(x) ≤S f(y), for
        all x and y in R . {\displaystyle \mathbb {R} .} </content>
      - >-
        <page_title> Rod calculus </page_title> <path> Rod_calculus >
        Subtraction > Without borrowing </path> <section_title> Without
        borrowing </section_title> <content> In situation in which no borrowing
        is needed, one only needs to take the number of rods in the subtrahend
        from the minuend. The result of the calculation is the difference. The
        adjacent image shows the steps in subtracting 23 from 54. </content>
  - source_sentence: >-
      For some constant $b$, if the minimum value of
      \[f(x)=\dfrac{x^2-2x+b}{x^2+2x+b}\] is $\tfrac12$, what is the maximum
      value of $f(x)$?
    sentences:
      - >-
        <page_title> Lagrangian multiplier </page_title> <path>
        Lagrange_multiplier > Examples > Example 1 </path> <section_title>
        Example 1 </section_title> <content> Evaluating the objective function f
        at these points yields f ( 2 2 , 2 2 ) = 2 , f ( − 2 2 , − 2 2 ) = − 2 .
        {\displaystyle f\left({\tfrac {\sqrt {2\ }}{2}},{\tfrac {\sqrt {2\
        }}{2}}\right)={\sqrt {2\ }}\ ,\qquad f\left(-{\tfrac {\sqrt {2\
        }}{2}},-{\tfrac {\sqrt {2\ }}{2}}\right)=-{\sqrt {2\ }}~.} Thus the
        constrained maximum is 2 {\displaystyle \ {\sqrt {2\ }}\ } and the
        constrained minimum is − 2 {\displaystyle -{\sqrt {2}}} . </content>
      - >-
        <page_title> Second degree polynomial </page_title> <path>
        Quadratic_function > Graph of the univariate function > Vertex > Maximum
        and minimum points </path> <section_title> Maximum and minimum points
        </section_title> <content> Using calculus, the vertex point, being a
        maximum or minimum of the function, can be obtained by finding the roots
        of the derivative: f ( x ) = a x 2 + b x + c ⇒ f ′ ( x ) = 2 a x + b
        {\displaystyle f(x)=ax^{2}+bx+c\quad \Rightarrow \quad f'(x)=2ax+b} x is
        a root of f '(x) if f '(x) = 0 resulting in x = − b 2 a {\displaystyle
        x=-{\frac {b}{2a}}} with the corresponding function value f ( x ) = a (
        − b 2 a ) 2 + b ( − b 2 a ) + c = c − b 2 4 a , {\displaystyle
        f(x)=a\left(-{\frac {b}{2a}}\right)^{2}+b\left(-{\frac
        {b}{2a}}\right)+c=c-{\frac {b^{2}}{4a}},} so again the vertex point
        coordinates, (h, k), can be expressed as ( − b 2 a , c − b 2 4 a ) .
        {\displaystyle \left(-{\frac {b}{2a}},c-{\frac {b^{2}}{4a}}\right).}
        </content>
      - >-
        <page_title> Dimer model </page_title> <path> Domino_tiling > Counting
        tilings of regions </path> <section_title> Counting tilings of regions
        </section_title> <content> The number of ways to cover an m × n
        {\displaystyle m\times n} rectangle with m n 2 {\displaystyle {\frac
        {mn}{2}}} dominoes, calculated independently by Temperley & Fisher
        (1961) and Kasteleyn (1961), is given by (sequence A099390 in the OEIS)
        When both m and n are odd, the formula correctly reduces to zero
        possible domino tilings. A special case occurs when tiling the 2 × n
        {\displaystyle 2\times n} rectangle with n dominoes: the sequence
        reduces to the Fibonacci sequence.Another special case happens for
        squares with m = n = 0, 2, 4, 6, 8, 10, 12, ... is These numbers can be
        found by writing them as the Pfaffian of an m n × m n {\displaystyle
        mn\times mn} skew-symmetric matrix whose eigenvalues can be found
        explicitly. This technique may be applied in many mathematics-related
        subjects, for example, in the classical, 2-dimensional computation of
        the dimer-dimer correlator function in statistical mechanics. The number
        of tilings of a region is very sensitive to boundary conditions, and can
        change dramatically with apparently insignificant changes in the shape
        of the region. This is illustrated by the number of tilings of an Aztec
        diamond of order n, where the number of tilings is 2(n + 1)n/2. If this
        is replaced by the "augmented Aztec diamond" of order n with 3 long rows
        in the middle rather than 2, the number of tilings drops to the much
        smaller number D(n,n), a Delannoy number, which has only exponential
        rather than super-exponential growth in n. For the "reduced Aztec
        diamond" of order n with only one long middle row, there is only one
        tiling. </content>
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 384 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Lysandrec/MNLP_M2_document_encoder")
# Run inference
sentences = [
    'For some constant $b$, if the minimum value of \\[f(x)=\\dfrac{x^2-2x+b}{x^2+2x+b}\\] is $\\tfrac12$, what is the maximum value of $f(x)$?',
    "<page_title> Second degree polynomial </page_title> <path> Quadratic_function > Graph of the univariate function > Vertex > Maximum and minimum points </path> <section_title> Maximum and minimum points </section_title> <content> Using calculus, the vertex point, being a maximum or minimum of the function, can be obtained by finding the roots of the derivative: f ( x ) = a x 2 + b x + c ⇒ f ′ ( x ) = 2 a x + b {\\displaystyle f(x)=ax^{2}+bx+c\\quad \\Rightarrow \\quad f'(x)=2ax+b} x is a root of f '(x) if f '(x) = 0 resulting in x = − b 2 a {\\displaystyle x=-{\\frac {b}{2a}}} with the corresponding function value f ( x ) = a ( − b 2 a ) 2 + b ( − b 2 a ) + c = c − b 2 4 a , {\\displaystyle f(x)=a\\left(-{\\frac {b}{2a}}\\right)^{2}+b\\left(-{\\frac {b}{2a}}\\right)+c=c-{\\frac {b^{2}}{4a}},} so again the vertex point coordinates, (h, k), can be expressed as ( − b 2 a , c − b 2 4 a ) . {\\displaystyle \\left(-{\\frac {b}{2a}},c-{\\frac {b^{2}}{4a}}\\right).} </content>",
    '<page_title> Lagrangian multiplier </page_title> <path> Lagrange_multiplier > Examples > Example 1 </path> <section_title> Example 1 </section_title> <content> Evaluating the objective function f at these points yields f ( 2 2 , 2 2 ) = 2 , f ( − 2 2 , − 2 2 ) = − 2 . {\\displaystyle f\\left({\\tfrac {\\sqrt {2\\ }}{2}},{\\tfrac {\\sqrt {2\\ }}{2}}\\right)={\\sqrt {2\\ }}\\ ,\\qquad f\\left(-{\\tfrac {\\sqrt {2\\ }}{2}},-{\\tfrac {\\sqrt {2\\ }}{2}}\\right)=-{\\sqrt {2\\ }}~.} Thus the constrained maximum is 2 {\\displaystyle \\ {\\sqrt {2\\ }}\\ } and the constrained minimum is − 2 {\\displaystyle -{\\sqrt {2}}} . </content>',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

This training dataset was synthetically generated. For each question from the source Q/A dataset (Lysandrec/MNLP_M2_rag_dataset), relevant passages were retrieved from a large document corpus (Lysandrec/MNLP_M2_rag_documents).

• A positive_passage was identified from the retrieved candidates, typically one containing the answer to the question. If no definitive positive was found, the top retrieved passage was often selected.
• Hard_negative_passages were selected from other highly-ranked (but not positive) retrieved documents for the same question.
• Random_negative_passages were sampled from the broader document corpus, ensuring they differed from the selected positive and hard negative passages. This process resulted in triplets of (query, positive_passage, negative_passage) used for training.

• Size: 100,000 training samples
• Columns: query (a question), positive_passage (a good retrieved document), and negative_passage (a bad example of a retrieved document)
• Approximate statistics based on the first 1000 samples:

  	query	positive_passage	negative_passage
  type	string	string	string
  details	min: 13 tokens mean: 64.12 tokens max: 214 tokens	min: 64 tokens mean: 205.95 tokens max: 384 tokens	min: 63 tokens mean: 178.5 tokens max: 384 tokens

• Samples:

  query	positive_passage	negative_passage
  The average of first five prime numbers greater than 61 is?<br>A. A)32.2<br>B. B)32.98<br>C. C)74.6<br>D. D)32.8<br>E. E)32.4	<page_title> 61 (number) </page_title> <path> 61_(number) > In mathematics </path> <section_title> In mathematics </section_title> <content> 61 is: the 18th prime number. a twin prime with 59. a cuban prime of the form p = x3 − y3/x − y, where x = y + 1. the smallest proper prime, a prime p which ends in the digit 1 in base 10 and whose reciprocal in base 10 has a repeating sequence with length p − 1. In such primes, each digit 0, 1, ..., 9 appears in the repeating sequence the same number of times as does each other digit (namely, p − 1/10 times). </content>	<page_title> Astatine </page_title> <path> Element_85 > Characteristics > Chemical </path> <section_title> Chemical </section_title> <content> In comparison, the value of Cl (349) is 6.4% higher than F (328); Br (325) is 6.9% less than Cl; and I (295) is 9.2% less than Br. The marked reduction for At was predicted as being due to spin–orbit interactions. The first ionization energy of astatine is about 899 kJ mol−1, which continues the trend of decreasing first ionization energies down the halogen group (fluorine, 1681; chlorine, 1251; bromine, 1140; iodine, 1008). </content>
  A charitable association sold an average of 66 raffle tickets per member. Among the female members, the average was 70 raffle tickets. The male to female ratio of the association is 1:2. What was the average number E of tickets sold by the male members of the association<br>A. A)50<br>B. B)56<br>C. C)58<br>D. D)62<br>E. E)66	<page_title> RSA number </page_title> <path> RSA_numbers </path> <section_title> Summary </section_title> <content> Cash prizes of varying size, up to US$200,000 (and prizes up to $20,000 awarded), were offered for factorization of some of them. The smallest RSA number was factored in a few days. Most of the numbers have still not been factored and many of them are expected to remain unfactored for many years to come. </content>	<page_title> Peer learning </page_title> <path> Peer_learning > Connections with other practices > Connectivism </path> <section_title> Connectivism </section_title> <content> Yochai Benkler explains how the now-ubiquitous computer helps us produce and process knowledge together with others in his book, The Wealth of Networks. George Siemens argues in Connectivism: A Learning Theory for the Digital Age, that technology has changed the way we learn, explaining how it tends to complicate or expose the limitations of the learning theories of the past. In practice, the ideas of connectivism developed in and alongside the then-new social formation, "massive open online courses" or MOOCs. Connectivism proposes that the knowledge we can access by virtue of our connections with others is just as valuable as the information carried inside our minds. </content>
  Find prime numbers \(a, b, c, d, e\) such that \(a^4 + b^4 + c^4 + d^4 + e^4 = abcde\).	<page_title> Pythagorean triangle </page_title> <path> Primitive_Pythagorean_triple > Special cases and related equations > The Jacobi–Madden equation </path> <section_title> The Jacobi–Madden equation </section_title> <content> The equation, a 4 + b 4 + c 4 + d 4 = ( a + b + c + d ) 4 {\displaystyle a^{4}+b^{4}+c^{4}+d^{4}=(a+b+c+d)^{4}} is equivalent to the special Pythagorean triple, ( a 2 + a b + b 2 ) 2 + ( c 2 + c d + d 2 ) 2 = ( ( a + b ) 2 + ( a + b ) ( c + d ) + ( c + d ) 2 ) 2 {\displaystyle (a^{2}+ab+b^{2})^{2}+(c^{2}+cd+d^{2})^{2}=((a+b)^{2}+(a+b)(c+d)+(c+d)^{2})^{2}} There is an infinite number of solutions to this equation as solving for the variables involves an elliptic curve. Small ones are, a , b , c , d = − 2634 , 955 , 1770 , 5400 {\displaystyle a,b,c,d=-2634,955,1770,5400} a , b , c , d = − 31764 , 7590 , 27385 , 48150 {\displaystyle a,b,c,d=-31764,7590,27385,48150} </content>	` Pythagorean triple Descartes' Circle Theorem For the case of Descartes' circle theorem where all variables are squares, 2 ( a 4 + b 4 + c 4 + d 4 ) = ( a 2 + b 2 + c 2 + d 2 ) 2 {\displaystyle 2(a^{4}+b^{4}+c^{4}+d^{4})=(a^{2}+b^{2}+c^{2}+d^{2})^{2}} Euler showed this is equivalent to three simultaneous Pythagorean triples, ( 2 a b ) 2 + ( 2 c d ) 2 = ( a 2 + b 2 − c 2 − d 2 ) 2 {\displaystyle (2ab)^{2}+(2cd)^{2}=(a^{2}+b^{2}-c^{2}-d^{2})^{2}} ( 2 a c ) 2 + ( 2 b d ) 2 = ( a 2 − b 2 + c 2 − d 2 ) 2 {\displaystyle (2ac)^{2}+(2bd)^{2}=(a^{2}-b^{2}+c^{2}-d^{2})^{2}} ( 2 a d ) 2 + ( 2 b c ) 2 = ( a 2 − b 2 − c 2 + d 2 ) 2 {\displaystyle (2ad)^{2}+(2bc)^{2}=(a^{2}-b^{2}-c^{2}+d^{2})^{2}} There is also an infinite number of solutions, and for the special case when a + b = c {\displaystyle a+b=c} , then the equation simplifi...

• Loss: TripletLoss with these parameters:

  {
      "distance_metric": "TripletDistanceMetric.EUCLIDEAN",
      "triplet_margin": 5
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• num_train_epochs: 1
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 64
• per_device_eval_batch_size: 64
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• tp_size: 0
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.3199	500	4.0855
0.6398	1000	3.9274
0.9597	1500	3.9199

Framework Versions

• Python: 3.12.8
• Sentence Transformers: 3.4.1
• Transformers: 4.51.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

TripletLoss

@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}