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.

python

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]

Training Details

Training Dataset

Unnamed Dataset

Size: 3,619 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 2 tokens
mean: 15.7 tokens
max: 38 tokens
min: 2 tokens
mean: 411.84 tokens
max: 3755 tokens

Samples:

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...

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "get_similarity" 4}

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: 8 tokens
mean: 16.0 tokens
max: 24 tokens
min: 6 tokens
mean: 642.47 tokens
max: 6152 tokens

Samples:

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

Loss: CachedMultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "get_similarity" 4}

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
0.2203	50	0.2209	-
0.4405	100	0.1635	0.0402
0.6608	150	0.1759	-
0.8811	200	0.1674	0.1307
1.1013	250	0.1134	-
1.3216	300	0.0809	0.0441
1.5419	350	0.0571	-
1.7621	400	0.077	0.0268
1.9824	450	0.0557	-

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

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",
}

CachedMultipleNegativesRankingLoss

bibtex

@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}
}

	anchor	positive
type	string	string
details	min: 2 tokens mean: 15.7 tokens max: 38 tokens	min: 2 tokens mean: 411.84 tokens max: 3755 tokens