lochhonest/finetuned_mbert

SentenceTransformer based on nomic-ai/modernbert-embed-base

This is a sentence-transformers model finetuned from nomic-ai/modernbert-embed-base. 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: nomic-ai/modernbert-embed-base
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 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()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'What is the primary purpose of the FITS format?',
    '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',
    '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',
]
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]

Training Details

Training Dataset

Unnamed Dataset

• Size: 3,625 training samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 2 tokens mean: 15.71 tokens max: 38 tokens	min: 2 tokens mean: 412.57 tokens max: 3755 tokens

• Samples:

  anchor	positive
  What is the purpose of the document described in the preface?	Preface This is the reference document describing the individual XMM-Newton Survey Science Centre (SSC) data product files. It is intended to be of use to software developers, archive administrators and to scientists analysing XMM-Newton data. Please see the SSC data products Interface Control Document (XMM-SOC-ICD-0006-SSC, issue 4.0) for a description of the product group files and other related files that are sent to the SOC. This version (4.3) includes changes related to the upgrade to SAS16.0 in the processing pipeline originally developped in 2012 to uniformly process all the XMM data at that time, from which the 3XMM catalogue was derived. Revisions and additions since version 4.2 are identified by change bars at the right of each page. This document will continue to evolve through subsequent issues, under indirect control from the SAS and SSC configuration control boards. This document is the result of the work of many people. Contributors have included: Hermann Brunner, G...
  What version of the document is described in the preface?	Preface This is the reference document describing the individual XMM-Newton Survey Science Centre (SSC) data product files. It is intended to be of use to software developers, archive administrators and to scientists analysing XMM-Newton data. Please see the SSC data products Interface Control Document (XMM-SOC-ICD-0006-SSC, issue 4.0) for a description of the product group files and other related files that are sent to the SOC. This version (4.3) includes changes related to the upgrade to SAS16.0 in the processing pipeline originally developped in 2012 to uniformly process all the XMM data at that time, from which the 3XMM catalogue was derived. Revisions and additions since version 4.2 are identified by change bars at the right of each page. This document will continue to evolve through subsequent issues, under indirect control from the SAS and SSC configuration control boards. This document is the result of the work of many people. Contributors have included: Hermann Brunner, G...
  What is the main change in version 4.3 of the document?	Preface This is the reference document describing the individual XMM-Newton Survey Science Centre (SSC) data product files. It is intended to be of use to software developers, archive administrators and to scientists analysing XMM-Newton data. Please see the SSC data products Interface Control Document (XMM-SOC-ICD-0006-SSC, issue 4.0) for a description of the product group files and other related files that are sent to the SOC. This version (4.3) includes changes related to the upgrade to SAS16.0 in the processing pipeline originally developped in 2012 to uniformly process all the XMM data at that time, from which the 3XMM catalogue was derived. Revisions and additions since version 4.2 are identified by change bars at the right of each page. This document will continue to evolve through subsequent issues, under indirect control from the SAS and SSC configuration control boards. This document is the result of the work of many people. Contributors have included: Hermann Brunner, G...

• Loss: CachedMultipleNegativesRankingLoss with these parameters:

  {
      "scale": 1.0,
      "similarity_fct": "get_similarity"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 30 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 30 samples:

  	anchor	positive
  type	string	string
  details	min: 10 tokens mean: 16.73 tokens max: 31 tokens	min: 6 tokens mean: 655.37 tokens max: 6762 tokens

• Samples:

  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 an additional offset is applied to reduce noise (the offset is later subtracted again by the SAS). In the MOS event lists this extension currently defines columns outside the sensitive CCD window, to which formal very high values of the offset are associated. These columns are discarded by the data processing. - For MOS imaging mode event lists this extension contains the following columns: Name Type Description --------- ---------------- ---------------------------------------------------------- RAWX 2-byte INTEGER Row or column of the bad offset OFFSETX 2-byte INTEGER amplitude of additional column offset (0 for row offset) OFFSETY 2-byte INTEGER amplitude of additional row offset (0 for column offset) CCDNR 1-byte CCD where the offset occurs - 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 RGS field of view. It also includes the entries for the proposal position and the on-axis location. EPIC and RGS positions are given, as well as RGS spatial and energy-dispersion angle extraction regions for the sources and a background region. - These files are identified using the keyword CONTENT = 'RGS SOURCE LIST' / File content in the primary header. - There are two binary table extensions (SRCLIST and RGSn_BACKGROUND), plus a further three binary table extensions per source (RGSn_SRCm_SPATIAL, RGSn_SRCm_ORDER_1 and RGSn_SRCm_ORDER_2, where n is the number of the RGS (1 or 2) and m is the number of the source. - The SRCLIST extension has the following columns: Name Type Description -------------- ------------------ --------------------------------------------------------- INDEX 2-byte INTEGER Source inde...
  What is the purpose of the analysis steps outlined in the document?	Structure of the document The structure of the present document is as follows: - Chapter [sasguide:par:analysis] introduces the investigator to the analysis of XMM-Newton (http://www.cosmos.esa.int/web/xmm-newton/technical-details) data. It provides a brief description of XMM-Newton (http://www.cosmos.esa.int/web/xmm-newton/technical-details) observation and calibration files and outlines the analysis steps required to produce calibrated event files and to extract scientific products. - Chapter [sasguide:par:gui] describes the SAS graphical user interface (GUI), a user friendly tool which enables SAS interactive analysis tasks to be run without using the command line. - Chapters [sasguide:par:epic], [sasguide:par:rgs] and [sasguide:par:om] describe the SAS analysis steps required to obtain EPIC (http://www.cosmos.esa.int/web/xmm-newton/technical-details-epic), RGS (http://www.cosmos.esa.int/web/xmm-newton/technical-details-r...

• Loss: CachedMultipleNegativesRankingLoss with these parameters:

  {
      "scale": 1.0,
      "similarity_fct": "get_similarity"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 4
• num_train_epochs: 2
• lr_scheduler_type: constant
• warmup_ratio: 0.1
• bf16: True
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 4
• 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: 1.0
• num_train_epochs: 2
• max_steps: -1
• lr_scheduler_type: constant
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• 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: True
• 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: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• 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: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

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

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.4.1
• Transformers: 4.49.0
• PyTorch: 2.6.0+cu124
• Accelerate: 1.4.0
• Datasets: 3.3.2
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

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

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}