Upload fine-tuned LoRA model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "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
+}

README.md

@@ -0,0 +1,731 @@
+---
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:500
+- loss:MultipleNegativesRankingLoss
+base_model: lufercho/ArxBert-MLM
+widget:
+- source_sentence: "Entanglement increase from local interactions with\n  not-completely-positive\
+    \ maps"
+  sentences:
+  - '  Simple examples are constructed that show the entanglement of two qubits
+
+    being both increased and decreased by interactions on just one of them. One of
+
+    the two qubits interacts with a third qubit, a control, that is never entangled
+
+    or correlated with either of the two entangled qubits and is never entangled,
+
+    but becomes correlated, with the system of those two qubits. The two entangled
+
+    qubits do not interact, but their state can change from maximally entangled to
+
+    separable or from separable to maximally entangled. Similar changes for the two
+
+    qubits are made with a swap operation between one of the qubits and a control;
+
+    then there are compensating changes of entanglement that involve the control.
+
+    When the entanglement increases, the map that describes the change of the state
+
+    of the two entangled qubits is not completely positive. Combination of two
+
+    independent interactions that individually give exponential decay of the
+
+    entanglement can cause the entanglement to not decay exponentially but,
+
+    instead, go to zero at a finite time.
+
+    '
+  - '  Many extra-solar planets discovered over the past decade are gas giants in
+
+    tight orbits around their host stars. Due to the difficulties of forming these
+
+    `hot Jupiters'' in situ, they are generally assumed to have migrated to their
+
+    present orbits through interactions with their nascent discs. In this paper, we
+
+    present a systematic study of giant planet migration in power law discs. We
+
+    find that the planetary migration rate is proportional to the disc surface
+
+    density. This is inconsistent with the assumption that the migration rate is
+
+    simply the viscous drift speed of the disc. However, this result can be
+
+    obtained by balancing the angular momentum of the planet with the viscous
+
+    torque in the disc. We have verified that this result is not affected by
+
+    adjusting the resolution of the grid, the smoothing length used, or the time at
+
+    which the planet is released to migrate.
+
+    '
+  - '  We investigate the evolution of binary fractions in star clusters using
+
+    N-body models of up to 100000 stars. Primordial binary frequencies in these
+
+    models range from 5% to 50%. Simulations are performed with the NBODY4 code and
+
+    include a full mass spectrum of stars, stellar evolution, binary evolution and
+
+    the tidal field of the Galaxy. We find that the overall binary fraction of a
+
+    cluster almost always remains close to the primordial value, except at late
+
+    times when a cluster is near dissolution. A critical exception occurs in the
+
+    central regions where we observe a marked increase in binary fraction with time
+
+    -- a simulation starting with 100000 stars and 5% binaries reached a core
+
+    binary frequency as high as 40% at the end of the core-collapse phase
+
+    (occurring at 16 Gyr with ~20000 stars remaining). Binaries are destroyed in
+
+    the core by a variety of processes as a cluster evolves, but the combination of
+
+    mass-segregation and creation of new binaries in exchange interactions produces
+
+    the observed increase in relative number. We also find that binaries are cycled
+
+    into and out of cluster cores in a manner that is analogous to convection in
+
+    stars. For models of 100000 stars we show that the evolution of the core-radius
+
+    up to the end of the initial phase of core-collapse is not affected by the
+
+    exact value of the primordial binary frequency (for frequencies of 10% or
+
+    less). We discuss the ramifications of our results for the likely primordial
+
+    binary content of globular clusters.
+
+    '
+- source_sentence: "Vortex proliferation in the Berezinskii-Kosterlitz-Thouless regime\
+    \ on a\n  two-dimensional lattice of Bose-Einstein condensates"
+  sentences:
+  - '  While the members of the Type IIn category of supernovae are united by the
+
+    presence of strong multicomponent Balmer emission lines in their spectra, they
+
+    are quite heterogeneous with respect to other properties such as Balmer line
+
+    profiles, light curves, strength of radio emission, and intrinsic brightness.
+
+    We are now beginning to see variety among SNe IIn in their polarimetric
+
+    characteristics as well, some but not all of which may be due to inclination
+
+    angle effects. The increasing number of known "hybrid" SNe with IIn-like
+
+    emission lines suggests that circumstellar material may be more common around
+
+    all types of SNe than previously thought. Investigation of the correlations
+
+    between spectropolarimetric signatures and other IIn attributes will help us
+
+    address the question of classification of "interacting SNe" and the possibility
+
+    of distinguishing different groups within the diverse IIn subclass.
+
+    '
+  - '  (Abridged) We compare recent results from X-ray, strong lensing, weak
+
+    lensing, and optical observations with numerical simulations of the merging
+
+    galaxy cluster 1E0657-56. X-ray observations reveal a bullet-like subcluster
+
+    with a prominent bow shock, while lensing results show that the positions of
+
+    the total mass peaks are consistent with the centroids of the collisionless
+
+    galaxies (and inconsistent with the X-ray brightness peaks). Previous studies,
+
+    based on older observational datasets, have placed upper limits on the
+
+    self-interaction cross-section of dark matter per unit mass, sigma/m, using
+
+    simplified analytic techniques. In this work, we take advantage of new,
+
+    higher-quality observational datasets by running N-body simulations of
+
+    1E0657-56 that include the effects of self-interacting dark matter, and
+
+    comparing the results with observations. Furthermore, the recent data allow for
+
+    a new independent method of constraining sigma/m, based on the non-observation
+
+    of an offset between the bullet subcluster mass peak and galaxy centroid. This
+
+    new method places an upper limit (68% confidence) of sigma/m < 1.25 cm^2/g. If
+
+    we make the assumption that the subcluster and the main cluster had equal
+
+    mass-to-light ratios prior to the merger, we derive our most stringent
+
+    constraint of sigma/m < 0.7 cm^2/g, which comes from the consistency of the
+
+    subcluster''s observed mass-to-light ratio with the main cluster''s, and with
+    the
+
+    universal cluster value, ruling out the possibility of a large fraction of dark
+
+    matter particles being scattered away due to collisions. Our limit is a slight
+
+    improvement over the previous result from analytic estimates, and rules out
+
+    most of the 0.5 - 5cm^2/g range invoked to explain inconsistencies between the
+
+    standard collisionless cold dark matter model and observations.
+
+    '
+  - '  We observe the proliferation of vortices in the
+
+    Berezinskii-Kosterlitz-Thouless regime on a two-dimensional array of
+
+    Josephson-coupled Bose-Einstein condensates. As long as the Josephson
+
+    (tunneling) energy J exceeds the thermal energy T, the array is vortex-free.
+
+    With decreasing J/T, vortices appear in the system in ever greater numbers. We
+
+    confirm thermal activation as the vortex formation mechanism and obtain
+
+    information on the size of bound vortex pairs as J/T is varied.
+
+    '
+- source_sentence: "Geometric Complexity Theory V: On deciding nonvanishing of a generalized\n\
+    \  Littlewood-Richardson coefficient"
+  sentences:
+  - '  I shall present three arguments for the proposition that intelligent life is
+
+    very rare in the universe. First, I shall summarize the consensus opinion of
+
+    the founders of the Modern Synthesis (Simpson, Dobzhanski, and Mayr) that the
+
+    evolution of intelligent life is exceedingly improbable. Second, I shall
+
+    develop the Fermi Paradox: if they existed they''d be here. Third, I shall show
+
+    that if intelligent life were too common, it would use up all available
+
+    resources and die out. But I shall show that the quantum mechanical principle
+
+    of unitarity (actually a form of teleology!) requires intelligent life to
+
+    survive to the end of time. Finally, I shall argue that, if the universe is
+
+    indeed accelerating, then survival to the end of time requires that intelligent
+
+    life, though rare, to have evolved several times in the visible universe. I
+
+    shall argue that the acceleration is a consequence of the excess of matter over
+
+    antimatter in the universe. I shall suggest experiments to test these claims.
+
+    '
+  - "  This article has been withdrawn because it has been merged with the earlier\n\
+    article GCT3 (arXiv: CS/0501076 [cs.CC]) in the series. The merged article is\n\
+    now available as:\n  Geometric Complexity Theory III: on deciding nonvanishing\
+    \ of a\nLittlewood-Richardson Coefficient, Journal of Algebraic Combinatorics,\
+    \ vol. 36,\nissue 1, 2012, pp. 103-110. (Authors: Ketan Mulmuley, Hari Narayanan\
+    \ and Milind\nSohoni)\n  The new article in this GCT5 slot in the series is:\n\
+    \  Geometric Complexity Theory V: Equivalence between blackbox derandomization\n\
+    of polynomial identity testing and derandomization of Noether's Normalization\n\
+    Lemma, in the Proceedings of FOCS 2012 (abstract), arXiv:1209.5993 [cs.CC]\n(full\
+    \ version) (Author: Ketan Mulmuley)\n"
+  - '  We use high-resolution near-infrared spectroscopy from Keck Observatory to
+
+    measure the stellar velocity dispersions of 19 super star clusters (SSCs) in
+
+    the nuclear starburst of M82. The clusters have ages on the order of 10 Myr,
+
+    which is many times longer than the crossing times implied by their velocity
+
+    dispersions and radii. We therefore apply the Virial Theorem to derive the
+
+    kinematic mass for 15 of the SSCs. The SSCs have masses of 2 x 10^5 to 4 x 10^6
+
+    solar masses, with a total population mass of 1.4 x 10^7 solar masses.
+
+    Comparison of the loci of the young M82 SSCs and old Milky Way globular
+
+    clusters in a plot of radius versus velocity dispersion suggests that the SSCs
+
+    are a population of potential globular clusters. We present the mass function
+
+    for the SSCs, and find a power law fit with an index of gamma = -1.91 +/- 0.06.
+
+    This result is nearly identical to the mass function of young SSCs in the
+
+    Antennae galaxies.
+
+    '
+- source_sentence: "Teleparallel Version of the Stationary Axisymmetric Solutions\
+    \ and their\n  Energy Contents"
+  sentences:
+  - '  We present a review of the discrete dipole approximation (DDA), which is a
+
+    general method to simulate light scattering by arbitrarily shaped particles. We
+
+    put the method in historical context and discuss recent developments, taking
+
+    the viewpoint of a general framework based on the integral equations for the
+
+    electric field. We review both the theory of the DDA and its numerical aspects,
+
+    the latter being of critical importance for any practical application of the
+
+    method. Finally, the position of the DDA among other methods of light
+
+    scattering simulation is shown and possible future developments are discussed.
+
+    '
+  - '  This work contains the teleparallel version of the stationary axisymmetric
+
+    solutions. We obtain the tetrad and the torsion fields representing these
+
+    solutions. The tensor, vector and axial-vector parts of the torsion tensor are
+
+    evaluated. It is found that the axial-vector has component only along $\rho$
+
+    and $z$ directions. The three possibilities of the axial vector depending on
+
+    the metric function $B$ are discussed. The vector related with spin has also
+
+    been evaluated and the corresponding extra Hamiltonian is furnished. Further,
+
+    we use the teleparallel version of M$\ddot{o}$ller prescription to find the
+
+    energy-momentum distribution of the solutions. It is interesting to note that
+
+    (for $\lambda=1$) energy and momentum densities in teleparallel theory are
+
+    equal to the corresponding quantities in GR plus an additional quantity in
+
+    each, which may become equal under certain conditions. Finally, we discuss the
+
+    two special cases of the stationary axisymmetric solutions.
+
+    '
+  - '  Most recently, both BaBar and Belle experiments found evidences of neutral
+
+    $D$ mixing. In this paper, we discuss the constraints on the strong phase
+
+    difference in $D^0 \to K\pi$ decay from the measurements of the mixing
+
+    parameters, $y^\prime$, $y_{CP}$ and $x$ at the $B$ factories. The sensitivity
+
+    of the measurement of the mixing parameter $y$ is estimated in BES-III
+
+    experiment at $\psi(3770)$ peak. We also make an estimate on the measurements
+
+    of the mixing rate $R_M$. Finally, the sensitivity of the strong phase
+
+    difference at BES-III are obtained by using data near the $D\bar{D}$ threshold
+
+    with CP tag technique at BES-III experiment.
+
+    '
+- source_sentence: "Approximation of the distribution of a stationary Markov process\
+    \ with\n  application to option pricing"
+  sentences:
+  - '  We build a sequence of empirical measures on the space D(R_+,R^d) of
+
+    R^d-valued c\`adl\`ag functions on R_+ in order to approximate the law of a
+
+    stationary R^d-valued Markov and Feller process (X_t). We obtain some general
+
+    results of convergence of this sequence. Then, we apply them to Brownian
+
+    diffusions and solutions to L\''evy driven SDE''s under some Lyapunov-type
+
+    stability assumptions. As a numerical application of this work, we show that
+
+    this procedure gives an efficient way of option pricing in stochastic
+
+    volatility models.
+
+    '
+  - '  We provide a new estimate of the local supermassive black hole mass function
+
+    using (i) the empirical relation between supermassive black hole mass and the
+
+    Sersic index of the host spheroidal stellar system and (ii) the measured
+
+    (spheroid) Sersic indices drawn from 10k galaxies in the Millennium Galaxy
+
+    Catalogue. The observational simplicity of our approach, and the direct
+
+    measurements of the black hole predictor quantity, i.e. the Sersic index, for
+
+    both elliptical galaxies and the bulges of disc galaxies makes it
+
+    straightforward to estimate accurate black hole masses in early- and late-type
+
+    galaxies alike. We have parameterised the supermassive black hole mass function
+
+    with a Schechter function and find, at the low-mass end, a logarithmic slope
+
+    (1+alpha) of ~0.7 for the full galaxy sample and ~1.0 for the early-type galaxy
+
+    sample. Considering spheroidal stellar systems brighter than M_B = -18 mag, and
+
+    integrating down to black hole masses of 10^6 M_sun, we find that the local
+
+    mass density of supermassive black holes in early-type galaxies rho_{bh,
+
+    early-type} = (3.5+/-1.2) x 10^5 h^3_{70} M_sun Mpc^{-3}, and in late-type
+
+    galaxies rho_{bh, late-type} = (1.0+/-0.5) x 10^5 h^3_{70} M_sun Mpc^{-3}. The
+
+    uncertainties are derived from Monte Carlo simulations which include
+
+    uncertainties in the M_bh-n relation, the catalogue of Sersic indices, the
+
+    galaxy weights and Malmquist bias. The combined, cosmological, supermassive
+
+    black hole mass density is thus Omega_{bh, total} = (3.2+/-1.2) x 10^{-6} h_70.
+
+    That is, using a new and independent method, we conclude that (0.007+/-0.003)
+
+    h^3_{70} per cent of the universe''s baryons are presently locked up in
+
+    supermassive black holes at the centres of galaxies.
+
+    '
+  - '  We treat Koll\''ar''s injectivity theorem from the analytic (or differential
+
+    geometric) viewpoint. More precisely, we give a curvature condition which
+
+    implies Koll\''ar type cohomology injectivity theorems. Our main theorem is
+
+    formulated for a compact K\"ahler manifold, but the proof uses the space of
+
+    harmonic forms on a Zariski open set with a suitable complete K\"ahler metric.
+
+    We need neither covering tricks, desingularizations, nor Leray''s spectral
+
+    sequence.
+
+    '
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+---
+
+# SentenceTransformer based on lufercho/ArxBert-MLM
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [lufercho/ArxBert-MLM](https://huggingface.co/lufercho/ArxBert-MLM). It maps sentences & paragraphs to a 768-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:** [lufercho/ArxBert-MLM](https://huggingface.co/lufercho/ArxBert-MLM) <!-- at revision a24b2f13eb71c311057a26155ae49bf16a0439ec -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, '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})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+    'Approximation of the distribution of a stationary Markov process with\n  application to option pricing',
+    "  We build a sequence of empirical measures on the space D(R_+,R^d) of\nR^d-valued c\\`adl\\`ag functions on R_+ in order to approximate the law of a\nstationary R^d-valued Markov and Feller process (X_t). We obtain some general\nresults of convergence of this sequence. Then, we apply them to Brownian\ndiffusions and solutions to L\\'evy driven SDE's under some Lyapunov-type\nstability assumptions. As a numerical application of this work, we show that\nthis procedure gives an efficient way of option pricing in stochastic\nvolatility models.\n",
+    "  We provide a new estimate of the local supermassive black hole mass function\nusing (i) the empirical relation between supermassive black hole mass and the\nSersic index of the host spheroidal stellar system and (ii) the measured\n(spheroid) Sersic indices drawn from 10k galaxies in the Millennium Galaxy\nCatalogue. The observational simplicity of our approach, and the direct\nmeasurements of the black hole predictor quantity, i.e. the Sersic index, for\nboth elliptical galaxies and the bulges of disc galaxies makes it\nstraightforward to estimate accurate black hole masses in early- and late-type\ngalaxies alike. We have parameterised the supermassive black hole mass function\nwith a Schechter function and find, at the low-mass end, a logarithmic slope\n(1+alpha) of ~0.7 for the full galaxy sample and ~1.0 for the early-type galaxy\nsample. Considering spheroidal stellar systems brighter than M_B = -18 mag, and\nintegrating down to black hole masses of 10^6 M_sun, we find that the local\nmass density of supermassive black holes in early-type galaxies rho_{bh,\nearly-type} = (3.5+/-1.2) x 10^5 h^3_{70} M_sun Mpc^{-3}, and in late-type\ngalaxies rho_{bh, late-type} = (1.0+/-0.5) x 10^5 h^3_{70} M_sun Mpc^{-3}. The\nuncertainties are derived from Monte Carlo simulations which include\nuncertainties in the M_bh-n relation, the catalogue of Sersic indices, the\ngalaxy weights and Malmquist bias. The combined, cosmological, supermassive\nblack hole mass density is thus Omega_{bh, total} = (3.2+/-1.2) x 10^{-6} h_70.\nThat is, using a new and independent method, we conclude that (0.007+/-0.003)\nh^3_{70} per cent of the universe's baryons are presently locked up in\nsupermassive black holes at the centres of galaxies.\n",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 500 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 500 samples:
+  |         | sentence_0                                                                        | sentence_1                                                                           |
+  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                               |
+  | details | <ul><li>min: 6 tokens</li><li>mean: 16.92 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 175.28 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                        | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
+  |:----------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Lifetime of doubly charmed baryons</code>                                                                                   | <code>  In this work, we evaluate the lifetimes of the doubly charmed baryons<br>$\Xi_{cc}^{+}$, $\Xi_{cc}^{++}$ and $\Omega_{cc}^{+}$. We carefully calculate<br>the non-spectator contributions at the quark level where the Cabibbo-suppressed<br>diagrams are also included. The hadronic matrix elements are evaluated in the<br>simple non-relativistic harmonic oscillator model. Our numerical results are<br>generally consistent with that obtained by other authors who used the diquark<br>model. However, all the theoretical predictions on the lifetimes are one order<br>larger than the upper limit set by the recent SELEX measurement. This<br>discrepancy would be clarified by the future experiment, if more accurate<br>experiment still confirms the value of the SELEX collaboration, there must be<br>some unknown mechanism to be explored.<br></code>                                                                                                                                                                                             |
+  | <code>Broadening the Higgs Boson with Right-Handed Neutrinos and a Higher<br>  Dimension Operator at the Electroweak Scale</code> | <code>  The existence of certain TeV suppressed higher-dimension operators may open<br>up new decay channels for the Higgs boson to decay into lighter right-handed<br>neutrinos. These channels may dominate over all other channels if the Higgs<br>boson is light. For a Higgs boson mass larger than $2 m_W$ the new decays are<br>subdominant yet still of interest. The right-handed neutrinos have macroscopic<br>decay lengths and decay mostly into final states containing leptons and quarks.<br>A distinguishing collider signature of this scenario is a pair of displaced<br>vertices violating lepton number. A general operator analysis is performed<br>using the minimal flavor violation hypothesis to illustrate that these novel<br>decay processes can occur while remaining consistent with experimental<br>constraints on lepton number violating processes. In this context the question<br>of whether these new decay modes dominate is found to depend crucially on the<br>approximate flavor symmetries of the right-handed neutrinos.<br></code> |
+  | <code>Infrared Evolution Equations: Method and Applications</code>                                                                | <code>  It is a brief review on composing and solving Infrared Evolution Equations.<br>They can be used in order to calculate amplitudes of high-energy reactions in<br>different kinematic regions in the double-logarithmic approximation.<br></code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 10
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: no
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `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`: 10
+- `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}
+- `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`: False
+- `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
+- `dispatch_batches`: None
+- `split_batches`: 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
+
+</details>
+
+### Framework Versions
+- Python: 3.10.12
+- Sentence Transformers: 3.3.1
+- Transformers: 4.46.2
+- PyTorch: 2.5.1+cu121
+- Accelerate: 1.1.1
+- Datasets: 3.1.0
+- Tokenizers: 0.20.3
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@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",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,26 @@
+{
+  "_name_or_path": "lufercho/ArxBert-MLM",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.46.2",
+    "pytorch": "2.5.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9b8894d656d5ba8f4dd9ba37012e6479637b6aff93c1a75e1153d75b03d7e40
+size 440317448

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "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,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}