This is a sentence-transformers model trained. It maps sentences & paragraphs to a 1024-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

Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 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': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'C C C =C C C C Branch1 #Branch2 C Branch1 C C Branch1 C C C O C C Ring1 O Branch1 C C C Branch1 C C C Ring1 P',
    ':0pentane Branch :2prop1ene Ring1 :2propane Ring2 :2isobutane Branch O pop pop Ring2 C Branch C pop Ring2 Ring1 Ring1 Ring1 =C pop pop pop',
    'CH0+1 C Branch O pop :1ethanol Ring1 C C Branch C pop :2ethanol Ring2 C Branch O pop Branch C Branch C pop Branch :1methanol Ring1 C Branch O pop Ring2 Ring1 Ring1 C Branch :1methanol pop Ring2 Ring1 C pop pop Ring1 P pop Ring1 #Branch pop pop',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

Size: 3,988 training samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 24 tokens
mean: 112.44 tokens
max: 397 tokens
min: 16 tokens
mean: 98.41 tokens
max: 316 tokens
min: 0.5
mean: 0.84
max: 1.0

Samples:

sentence1	sentence2	label
`C C =C Branch1 C Br C =C Branch2 Ring1 =Branch2 C@@H1 Branch1 #Branch1 C C =Branch1 C =O O N C =Branch1 C =O O C C =C C =C C =C Ring1 =Branch1 C =C Ring2 Ring1 #Branch1`	`:0propane Ring1 =C Branch Br pop :2prop1ene Ring2 CH1 Branch :0acetic acid pop :0formamide Ring1 :6phenylmethanol pop pop Ring2 =Ring1 =Branch pop pop`	`1.0`
`C C =C Branch2 Ring2 Ring1 C =Branch1 C =O N C C N Branch2 Ring1 Ring2 C@@H1 Branch1 C C C =Branch1 C =O N C =Branch1 C =O N C C C Ring1 Ring1 C C Ring1 P O C =N Ring2 Ring1 Branch2`	`:0ethanamine C :=1methanol Ring1 C Branch C =Branch =O pop :5piperazine Branch CH1 Branch C pop :1formamide Ring1 :4Ncyclopropylformamide pop pop pop =Ring1 Branch pop pop`	`1.0`
`C C C Branch1 C N C C =C C Branch1 =C O C C C =Branch1 C =O N C C C Ring1 Ring1 =C C =C Ring1 #C`	`:0propan2amine Branch :0fluoromethane pop Ring1 :1prop1ene Ring1 :1ethene Ring1 C Branch O :1propionamide Ring2 :0cyclopropane pop pop =Ring1 =Branch pop pop pop`	`0.9597855227882037`

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

Evaluation Dataset

Unnamed Dataset

Size: 3,988 evaluation samples
Columns: sentence1, sentence2, and label
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 label
type string string float
details
min: 24 tokens
mean: 112.44 tokens
max: 397 tokens
min: 16 tokens
mean: 98.41 tokens
max: 316 tokens
min: 0.5
mean: 0.84
max: 1.0

Samples:

sentence1	sentence2	label
`C C =C Branch1 C Br C =C Branch2 Ring1 =Branch2 C@@H1 Branch1 #Branch1 C C =Branch1 C =O O N C =Branch1 C =O O C C =C C =C C =C Ring1 =Branch1 C =C Ring2 Ring1 #Branch1`	`:0propane Ring1 =C Branch Br pop :2prop1ene Ring2 CH1 Branch :0acetic acid pop :0formamide Ring1 :6phenylmethanol pop pop Ring2 =Ring1 =Branch pop pop`	`1.0`
`C C =C Branch2 Ring2 Ring1 C =Branch1 C =O N C C N Branch2 Ring1 Ring2 C@@H1 Branch1 C C C =Branch1 C =O N C =Branch1 C =O N C C C Ring1 Ring1 C C Ring1 P O C =N Ring2 Ring1 Branch2`	`:0ethanamine C :=1methanol Ring1 C Branch C =Branch =O pop :5piperazine Branch CH1 Branch C pop :1formamide Ring1 :4Ncyclopropylformamide pop pop pop =Ring1 Branch pop pop`	`1.0`
`C C C Branch1 C N C C =C C Branch1 =C O C C C =Branch1 C =O N C C C Ring1 Ring1 =C C =C Ring1 #C`	`:0propan2amine Branch :0fluoromethane pop Ring1 :1prop1ene Ring1 :1ethene Ring1 C Branch O :1propionamide Ring2 :0cyclopropane pop pop =Ring1 =Branch pop pop pop`	`0.9597855227882037`

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

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
gradient_accumulation_steps: 4
eval_accumulation_steps: 4
learning_rate: 3e-05
weight_decay: 0.01
num_train_epochs: 1
warmup_ratio: 0.1
save_safetensors: False
fp16: True
dataloader_drop_last: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 4
eval_accumulation_steps: 4
torch_empty_cache_steps: None
learning_rate: 3e-05
weight_decay: 0.01
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
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: False
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: False
fp16: True
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: True
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}
tp_size: 0
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
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Validation Loss
0.9960	62	3.7611

Framework Versions

Python: 3.13.2
Sentence Transformers: 4.1.0
Transformers: 4.51.3
PyTorch: 2.7.0+cu126
Accelerate: 1.6.0
Datasets: 3.6.0
Tokenizers: 0.21.1

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",
}

AnglELoss

bibtex

@misc{li2023angleoptimized,
    title={AnglE-optimized Text Embeddings},
    author={Xianming Li and Jing Li},
    year={2023},
    eprint={2309.12871},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	sentence1	sentence2	label
type	string	string	float
details	min: 24 tokens mean: 112.44 tokens max: 397 tokens	min: 16 tokens mean: 98.41 tokens max: 316 tokens	min: 0.5 mean: 0.84 max: 1.0

korolewadim/gselformer-large

SentenceTransformer

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Training Details

Training Dataset

Unnamed Dataset

Evaluation Dataset

Unnamed Dataset

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Framework Versions

Citation

BibTeX

Sentence Transformers

AnglELoss

korolewadim/
gselformer-large