README.md

---
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}
}
```

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