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SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(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})
(2): Normalize()
)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 'What is the primary purpose of the FITS format?',
8 'FITS format\n\nAll of the ODF/SDF component files, with the exception of the summary\nfiles, reconstructed orbit file, and raw attitude file, are FITS files\nand conform to the standard. A description of the FITS format can be\nfound in , which is accessible also at the URL\nThe calibration files and the bulk of the PPS products also conform to\nthe FITS standard. Wherever possible and desirable the calibration files\nand the PPS products follow the conventions of the OGIP\n(http://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/ofwg_intro.html) (Office\nof Guest Investigator Programs) FITS working group. The HEASARC FITS\nWorking Group activities are described at the following URL:\nFor FITS files where OGIP FITS standards are not applicable or\navailable, new standards closely following the OGIP approach are used.\n\nThe FITS format is primarily designed to store scientific data sets\nconsisting of multidimensional arrays (1-D spectra, 2-D images or 3-D\ndata cubes) and 2-dimensional tables containing rows and columns of\ndata. A FITS data file is composed of a sequence of Header + Data Units\n(HDUs).\n\nThe general structure of a FITS file is as follows:\n\n- a primary header;\n\n- a primary data array of zero length;\n\n- zero or more extensions\n\nEach extension consists of an extension header and a data section.\nExtensions are named and can appear in any order. Only the following\nFITS extensions are used:\n\n- ASCII table: XTENSION=TABLE\n\n- binary table: XTENSION=BINTABLE\n\n- image: XTENSION=IMAGE\n\nThe header consists of keyword=value statements, which describe the\norganisation of the data in the HDU and the format of the contents. It\nmay also provide additional information, for example, about instrument\nstatus or the history of the data. The following block contains the\ndata, which are structured as specified in the header. The data section\nof the HDU may contain a digital image, a table or a multidimensional\nmatrix that is not an image. An HDU need not contain data.\n',
9 'ASCII\n\nASCII files are used to present script and some tabular information. In\nparticular, each ODF/SDF contains a single summary file, with a summary\nof the information relating to the observation or slew (see\nSect.\xa0[dfhb:par:odf]).\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]anchor and positive| anchor | positive | |
|---|---|---|
| type | string | string |
| details |
|
|
| anchor | positive |
|---|---|
What is the purpose of the document described in the preface? | Preface[object Object][object Object]This is the reference document describing the individual XMM-Newton[object Object]Survey Science Centre (SSC) data product files. It is intended to be of[object Object]use to software developers, archive administrators and to scientists[object Object]analysing XMM-Newton data. Please see the SSC data products Interface[object Object]Control Document (XMM-SOC-ICD-0006-SSC, issue 4.0) for a description of[object Object]the product group files and other related files that are sent to the[object Object]SOC.[object Object][object Object]This version (4.3) includes changes related to the upgrade to SAS16.0 in[object Object]the processing pipeline originally developped in 2012 to uniformly[object Object]process all the XMM data at that time, from which the 3XMM catalogue was[object Object]derived. Revisions and additions since version 4.2 are identified by[object Object]change bars at the right of each page.[object Object][object Object]This document will continue to evolve through subsequent issues, under[object Object]indirect control from the SAS and SSC configuration control boards.[object Object][object Object]This document is the result of the work of many people. Contributors[object Object]have included:[object Object][object Object]Hermann Brunner, G... |
What version of the document is described in the preface? | Preface[object Object][object Object]This is the reference document describing the individual XMM-Newton[object Object]Survey Science Centre (SSC) data product files. It is intended to be of[object Object]use to software developers, archive administrators and to scientists[object Object]analysing XMM-Newton data. Please see the SSC data products Interface[object Object]Control Document (XMM-SOC-ICD-0006-SSC, issue 4.0) for a description of[object Object]the product group files and other related files that are sent to the[object Object]SOC.[object Object][object Object]This version (4.3) includes changes related to the upgrade to SAS16.0 in[object Object]the processing pipeline originally developped in 2012 to uniformly[object Object]process all the XMM data at that time, from which the 3XMM catalogue was[object Object]derived. Revisions and additions since version 4.2 are identified by[object Object]change bars at the right of each page.[object Object][object Object]This document will continue to evolve through subsequent issues, under[object Object]indirect control from the SAS and SSC configuration control boards.[object Object][object Object]This document is the result of the work of many people. Contributors[object Object]have included:[object Object][object Object]Hermann Brunner, G... |
What is the main change in version 4.3 of the document? | Preface[object Object][object Object]This is the reference document describing the individual XMM-Newton[object Object]Survey Science Centre (SSC) data product files. It is intended to be of[object Object]use to software developers, archive administrators and to scientists[object Object]analysing XMM-Newton data. Please see the SSC data products Interface[object Object]Control Document (XMM-SOC-ICD-0006-SSC, issue 4.0) for a description of[object Object]the product group files and other related files that are sent to the[object Object]SOC.[object Object][object Object]This version (4.3) includes changes related to the upgrade to SAS16.0 in[object Object]the processing pipeline originally developped in 2012 to uniformly[object Object]process all the XMM data at that time, from which the 3XMM catalogue was[object Object]derived. Revisions and additions since version 4.2 are identified by[object Object]change bars at the right of each page.[object Object][object Object]This document will continue to evolve through subsequent issues, under[object Object]indirect control from the SAS and SSC configuration control boards.[object Object][object Object]This document is the result of the work of many people. Contributors[object Object]have included:[object Object][object Object]Hermann Brunner, G... |
CachedMultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 1.0,
3 "similarity_fct": "get_similarity"
4}anchor and positive| anchor | positive | |
|---|---|---|
| type | string | string |
| details |
|
|
| anchor | positive |
|---|---|
In pn imaging mode event lists, what is the type of the OFFSETX column? | - In pn event lists this extension contains the CCD columns to which[object Object] an additional offset is applied to reduce noise (the offset is later[object Object] subtracted again by the SAS). In the MOS event lists this extension[object Object] currently defines columns outside the sensitive CCD window, to which[object Object] formal very high values of the offset are associated. These columns[object Object] are discarded by the data processing.[object Object][object Object]- For MOS imaging mode event lists this extension contains the[object Object] following columns:[object Object][object Object] Name Type Description[object Object] --------- ---------------- ----------------------------------------------------------[object Object] RAWX 2-byte INTEGER Row or column of the bad offset[object Object] OFFSETX 2-byte INTEGER amplitude of additional column offset (0 for row offset)[object Object] OFFSETY 2-byte INTEGER amplitude of additional row offset (0 for column offset)[object Object] CCDNR 1-byte CCD where the offset occurs[object Object][object Object]- For MOS timing mode event lists this extension contains the... |
What are the three binary table extensions created per source used for? | - This product lists bright sources detected by EPIC which fall in the[object Object] RGS field of view. It also includes the entries for the proposal[object Object] position and the on-axis location. EPIC and RGS positions are given,[object Object] as well as RGS spatial and energy-dispersion angle extraction[object Object] regions for the sources and a background region.[object Object][object Object]- These files are identified using the keyword[object Object][object Object] CONTENT = 'RGS SOURCE LIST' / File content[object Object][object Object] in the primary header.[object Object][object Object]- There are two binary table extensions (SRCLIST and RGSn_BACKGROUND),[object Object] plus a further three binary table extensions per source[object Object] (RGSn_SRCm_SPATIAL, RGSn_SRCm_ORDER_1 and RGSn_SRCm_ORDER_2, where n[object Object] is the number of the RGS (1 or 2) and m is the number of the source.[object Object][object Object]- The SRCLIST extension has the following columns:[object Object][object Object] Name Type Description[object Object] -------------- ------------------ ---------------------------------------------------------[object Object] INDEX 2-byte INTEGER Source inde... |
What is the purpose of the analysis steps outlined in the document? | Structure of the document[object Object][object Object]The structure of the present document is as follows:[object Object][object Object]- Chapter [sasguide:par:analysis] introduces the investigator to the[object Object] analysis of XMM-Newton[object Object] ([object Object]) data.[object Object] It provides a brief description of XMM-Newton[object Object] ([object Object])[object Object] observation and calibration files and outlines the analysis steps[object Object] required to produce calibrated event files and to extract scientific[object Object] products.[object Object][object Object]- Chapter [sasguide:par:gui] describes the SAS graphical user[object Object] interface (GUI), a user friendly tool which enables SAS interactive[object Object] analysis tasks to be run without using the command line.[object Object][object Object]- Chapters [sasguide:par:epic], [sasguide:par:rgs] and[object Object] [sasguide:par:om] describe the SAS analysis steps required to obtain[object Object] EPIC[object Object] ([object Object]),[object Object] RGS ([object Object]... |
CachedMultipleNegativesRankingLoss with these parameters:
1{
2 "scale": 1.0,
3 "similarity_fct": "get_similarity"
4}eval_strategy: stepsper_device_train_batch_size: 16per_device_eval_batch_size: 4num_train_epochs: 2lr_scheduler_type: constantwarmup_ratio: 0.1bf16: Truebatch_sampler: no_duplicatesoverwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 16per_device_eval_batch_size: 4per_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: 1.0num_train_epochs: 2max_steps: -1lr_scheduler_type: constantlr_scheduler_kwargs: {}warmup_ratio: 0.1warmup_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: Truefp16: 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: Nonehub_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: no_duplicatesmulti_dataset_batch_sampler: proportional| Epoch | Step | Training Loss | Validation Loss |
|---|---|---|---|
| 0.0441 | 10 | 2.3929 | - |
| 0.0881 | 20 | 2.2876 | - |
| 0.1322 | 30 | 2.2502 | - |
| 0.1762 | 40 | 2.2265 | - |
| 0.2203 | 50 | 2.176 | 0.9569 |
| 0.2643 | 60 | 2.1931 | - |
| 0.3084 | 70 | 2.1666 | - |
| 0.3524 | 80 | 2.1637 | - |
| 0.3965 | 90 | 2.1684 | - |
| 0.4405 | 100 | 2.1373 | 0.9265 |
| 0.4846 | 110 | 2.135 | - |
| 0.5286 | 120 | 2.1159 | - |
| 0.5727 | 130 | 2.113 | - |
| 0.6167 | 140 | 2.098 | - |
| 0.6608 | 150 | 2.0931 | 0.9054 |
| 0.7048 | 160 | 2.0954 | - |
| 0.7489 | 170 | 2.0882 | - |
| 0.7930 | 180 | 2.0926 | - |
| 0.8370 | 190 | 2.1139 | - |
| 0.8811 | 200 | 2.1151 | 0.8745 |
| 0.9251 | 210 | 2.1033 | - |
| 0.9692 | 220 | 2.1014 | - |
| 1.0132 | 230 | 2.0139 | - |
| 1.0573 | 240 | 2.0408 | - |
| 1.1013 | 250 | 2.0257 | 0.9039 |
| 1.1454 | 260 | 2.0401 | - |
| 1.1894 | 270 | 2.0189 | - |
| 1.2335 | 280 | 2.0521 | - |
| 1.2775 | 290 | 2.055 | - |
| 1.3216 | 300 | 2.0407 | 0.9321 |
| 1.3656 | 310 | 2.0252 | - |
| 1.4097 | 320 | 2.0126 | - |
| 1.4537 | 330 | 2.0431 | - |
| 1.4978 | 340 | 2.0293 | - |
| 1.5419 | 350 | 2.042 | 0.9105 |
| 1.5859 | 360 | 2.0557 | - |
| 1.6300 | 370 | 2.0481 | - |
| 1.6740 | 380 | 2.0169 | - |
| 1.7181 | 390 | 2.0402 | - |
| 1.7621 | 400 | 2.0376 | 0.8873 |
| 1.8062 | 410 | 2.045 | - |
| 1.8502 | 420 | 1.9934 | - |
| 1.8943 | 430 | 2.0335 | - |
| 1.9383 | 440 | 2.0278 | - |
| 1.9824 | 450 | 2.0313 | 0.8658 |
1@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{gao2021scaling,
2 title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
3 author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
4 year={2021},
5 eprint={2101.06983},
6 archivePrefix={arXiv},
7 primaryClass={cs.LG}
8}