modernbert-finetuned-for-sas

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 <!-- at revision d556a88e332558790b210f7bdbe87da2fa94a8d8 -->
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

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("lochhonest/modernbert-finetuned-for-sas")
# Run inference
sentences = [
    'In nearly all cases, how many source and background region spectra are supplied for the RGS?',
    'RGS spectral products\n\nThis section describes the spectral data products to be generated from\npointed observations.\n\nSource and background region spectra and a background-subtracted source\nspectrum are supplied for the brightest point sources in the RGS (in\nnearly all cases this is just one source). Spectral response matrices\nare also supplied.\n',
    "-   This extension gives the good time intervals for the event list.\n\n-   There is one extension per CCD in the relevant mode (IMAGING or\n    TIMING) during the exposure.\n\n-   The following keywords are present:\n\n        HDUCLASS= 'OGIP    '           / format conforms to OGIP standard\n        HDUCLAS1= 'GTI     '           / table contains Good Time Intervals\n        HDUCLAS2= 'STANDARD'           / standard Good Time Interval table\n\n-   This extension contains the following columns:\n\n      Name    Type          Description\n      ------- ------------- --------------------------------\n      START   8-byte REAL   seconds (since reference time)\n      STOP    8-byte REAL   seconds (since reference time)\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: 3,619 training samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	<ul><li>min: 2 tokens</li><li>mean: 15.7 tokens</li><li>max: 38 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 411.84 tokens</li><li>max: 3755 tokens</li></ul>

• Samples:

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

• Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "get_similarity"
  }
  

Evaluation Dataset

Unnamed Dataset

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

  	anchor	positive
  type	string	string
  details	<ul><li>min: 8 tokens</li><li>mean: 16.0 tokens</li><li>max: 24 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 642.47 tokens</li><li>max: 6152 tokens</li></ul>

• Samples:

  anchor	positive
  <code>What is the purpose of the PPS cross-correlation products?</code>	<code>General cross-correlation products<br><br>These PPS cross-correlation products list the names of all catalogues<br>searched (both around each EPIC position and in the whole EPIC field)<br>and describe the format of their output.<br></code>
  <code>What are the task parameters of rgssources?</code>	<code>rgssources<br>## Parameters<br><br> \label{rgssources:description:parameters}<br> <br> filemode} {modify (Optional): no<br>(Type: <br> Controls whether the task opens a previous source list for editing or creates a new one.<br> }<br> \optparm{changeprime} {no} {boolean} {yes
  <code>How many stars were used in the U-filter analysis for the G153 pointing to create the distortion map?</code>	<code>OM distortion<br><br>The  OM<br>(http://www.cosmos.esa.int/web/xmm-newton/technical-details-om) optics,<br>filters and (primarily) the detector system result in a certain amount<br>of image distortion. This effect can be corrected with a “distortion<br>map”, by comparing the expected position with the measured position for<br>a large number of stars in the OM<br>(http://www.cosmos.esa.int/web/xmm-newton/technical-details-om) field of<br>view. A U-filter analysis has been performed on the G153 pointing with<br>813 stars. The effect of applying this correction is shown in<br>Fig. [fig:uhb:distmap]. A positional r.m.s. accuracy of 0.5 − 1.5 arcsec<br>is obtained. The distortion map has been entered into the appropriate<br>CCF file and is used in http://www.cosmos.esa.int/web/xmm-newton/sas<br>(http://www.cosmos.esa.int/web/xmm-newton/sas).<br></code>

• Loss: <code>CachedMultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "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

<details><summary>Click to expand</summary>

• 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

</details>

Training Logs

Framework Versions

• Python: 3.10.14
• Sentence Transformers: 3.4.1
• Transformers: 4.48.2
• PyTorch: 2.6.0+cu124
• Accelerate: 1.3.0
• Datasets: 3.3.1
• 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}
}

<!-- ## 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.* -->