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})
)pip install -U sentence-transformers1from sentence_transformers import SentenceTransformer
2
3# Download from the 🤗 Hub
4model = SentenceTransformer("sentence_transformers_model_id")
5# Run inference
6sentences = [
7 'Approximation of the distribution of a stationary Markov process with\n application to option pricing',
8 " 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",
9 " 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",
10]
11embeddings = model.encode(sentences)
12print(embeddings.shape)
13# [3, 768]
14
15# Get the similarity scores for the embeddings
16similarities = model.similarity(embeddings, embeddings)
17print(similarities.shape)
18# [3, 3]sentence_0 and sentence_1| sentence_0 | sentence_1 | |
|---|---|---|
| type | string | string |
| details |
|
|
| sentence_0 | sentence_1 |
|---|---|
Lifetime of doubly charmed baryons | In this work, we evaluate the lifetimes of the doubly charmed baryons[object Object]$\Xi_{cc}^{+}$, $\Xi_{cc}^{++}$ and $\Omega_{cc}^{+}$. We carefully calculate[object Object]the non-spectator contributions at the quark level where the Cabibbo-suppressed[object Object]diagrams are also included. The hadronic matrix elements are evaluated in the[object Object]simple non-relativistic harmonic oscillator model. Our numerical results are[object Object]generally consistent with that obtained by other authors who used the diquark[object Object]model. However, all the theoretical predictions on the lifetimes are one order[object Object]larger than the upper limit set by the recent SELEX measurement. This[object Object]discrepancy would be clarified by the future experiment, if more accurate[object Object]experiment still confirms the value of the SELEX collaboration, there must be[object Object]some unknown mechanism to be explored.[object Object] |
Broadening the Higgs Boson with Right-Handed Neutrinos and a Higher[object Object] Dimension Operator at the Electroweak Scale | The existence of certain TeV suppressed higher-dimension operators may open[object Object]up new decay channels for the Higgs boson to decay into lighter right-handed[object Object]neutrinos. These channels may dominate over all other channels if the Higgs[object Object]boson is light. For a Higgs boson mass larger than $2 m_W$ the new decays are[object Object]subdominant yet still of interest. The right-handed neutrinos have macroscopic[object Object]decay lengths and decay mostly into final states containing leptons and quarks.[object Object]A distinguishing collider signature of this scenario is a pair of displaced[object Object]vertices violating lepton number. A general operator analysis is performed[object Object]using the minimal flavor violation hypothesis to illustrate that these novel[object Object]decay processes can occur while remaining consistent with experimental[object Object]constraints on lepton number violating processes. In this context the question[object Object]of whether these new decay modes dominate is found to depend crucially on the[object Object]approximate flavor symmetries of the right-handed neutrinos.[object Object] |
Infrared Evolution Equations: Method and Applications | It is a brief review on composing and solving Infrared Evolution Equations.[object Object]They can be used in order to calculate amplitudes of high-energy reactions in[object Object]different kinematic regions in the double-logarithmic approximation.[object Object] |
MultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 20.0,
3 "similarity_fct": "cos_sim"
4}per_device_train_batch_size: 16per_device_eval_batch_size: 16num_train_epochs: 10multi_dataset_batch_sampler: round_robinoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: noprediction_loss_only: Trueper_device_train_batch_size: 16per_device_eval_batch_size: 16per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1num_train_epochs: 10max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.0warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Falsefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Falsehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: Nonebatch_sampler: batch_samplermulti_dataset_batch_sampler: round_robin1@inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9}1@misc{henderson2017efficient,
2 title={Efficient Natural Language Response Suggestion for Smart Reply},
3 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},
4 year={2017},
5 eprint={1705.00652},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL}
8}