gselformer-large

SentenceTransformer

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

<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->

• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 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': 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.

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]

<!-- ### 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: 3,988 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

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

• Samples:

  sentence1	sentence2	label
  <code>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</code>	<code>:0propane Ring1 =C Branch Br pop :2prop1ene Ring2 CH1 Branch :0acetic acid pop :0formamide Ring1 :6phenylmethanol pop pop Ring2 =Ring1 =Branch pop pop</code>	<code>1.0</code>
  <code>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</code>	<code>: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</code>	<code>1.0</code>
  <code>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</code>	<code>:0propan2amine Branch :0fluoromethane pop Ring1 :1prop1ene Ring1 :1ethene Ring1 C Branch O :1propionamide Ring2 :0cyclopropane pop pop =Ring1 =Branch pop pop pop</code>	<code>0.9597855227882037</code>

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

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

Evaluation Dataset

Unnamed Dataset

• Size: 3,988 evaluation samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

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

• Samples:

  sentence1	sentence2	label
  <code>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</code>	<code>:0propane Ring1 =C Branch Br pop :2prop1ene Ring2 CH1 Branch :0acetic acid pop :0formamide Ring1 :6phenylmethanol pop pop Ring2 =Ring1 =Branch pop pop</code>	<code>1.0</code>
  <code>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</code>	<code>: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</code>	<code>1.0</code>
  <code>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</code>	<code>:0propan2amine Branch :0fluoromethane pop Ring1 :1prop1ene Ring1 :1ethene Ring1 C Branch O :1propionamide Ring2 :0cyclopropane pop pop =Ring1 =Branch pop pop pop</code>	<code>0.9597855227882037</code>

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

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

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

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

• 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

</details>

Training Logs

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

@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

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

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