ModernBERT-base-CTSP

SentenceTransformer based on answerdotai/ModernBERT-base

This is a sentence-transformers model finetuned from answerdotai/ModernBERT-base on the tekgen-ctsp dataset. 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: answerdotai/ModernBERT-base <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • tekgen-ctsp

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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})
)

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("YesaOuO/ModernBERT-base-CTSP")
# Run inference
sentences = [
    'In 1842 Alvars married the harpist Melanie Lewy, a member of a Vienna-based family of musicians with whom Alvars frequently performed.',
    'Elias Parish Alvars spouse Melanie Lewy, place of death Vienna.',
    'Elias Parish Alvars place of birth Teignmouth.',
]
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.* -->

Evaluation

Metrics

Triplet

• Dataset: YesaOuO/TEKGEN-CTSP
• Evaluated with <code>TripletEvaluator</code>

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Training Details

Training Dataset

tekgen-ctsp

• Dataset: tekgen-ctsp at 8d091eb
• Size: 1,136,292 training samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 11 tokens</li><li>mean: 38.01 tokens</li><li>max: 128 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 17.07 tokens</li><li>max: 47 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 17.02 tokens</li><li>max: 47 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>1976 Swedish Grand Prix was the seventh round of the 1976 Formula One season and the ninth Swedish Grand Prix.</code>	<code>1976 Swedish Grand Prix point in time 13 June 1976, part of 1976 Formula One season.</code>	<code>1976 Swedish Grand Prix pole position Jody Scheckter, winner Jody Scheckter.</code>
  <code>1976 Swedish Grand Prix was the seventh round of the 1976 Formula One season and the ninth Swedish Grand Prix.</code>	<code>1976 Swedish Grand Prix point in time 13 June 1976, part of 1976 Formula One season.</code>	<code>1976 Swedish Grand Prix point in time 13 June 1976, country Sweden.</code>
  <code>1976 Swedish Grand Prix was the seventh round of the 1976 Formula One season and the ninth Swedish Grand Prix.</code>	<code>1976 Swedish Grand Prix point in time 13 June 1976, part of 1976 Formula One season.</code>	<code>1976 Swedish Grand Prix point in time 13 June 1976.</code>

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

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

Evaluation Dataset

tekgen-ctsp

• Dataset: tekgen-ctsp at 8d091eb
• Size: 10,866 evaluation samples
• Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	<ul><li>min: 13 tokens</li><li>mean: 40.18 tokens</li><li>max: 183 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 19.82 tokens</li><li>max: 62 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 19.82 tokens</li><li>max: 62 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>Two men with prior criminal records, Dieter Degowski and Hans-Jürgen Rösner, went on the run for two days through Germany and the Netherlands.</code>	<code>Gladbeck hostage crisis country Netherlands, country Germany, participant Hans-Jürgen Rösner, participant Dieter Degowski.</code>	<code>Gladbeck hostage crisis end time 18 August 1988, point in time 18 August 1988, country Germany, start time 16 August 1988.</code>
  <code>The Gladbeck hostage crisis (known in Germany as the Gladbeck hostage drama) was a hostage-taking crisis that happened in August 1988 after an armed bank raid in Gladbeck, North Rhine-Westphalia, West Germany.</code>	<code>Gladbeck hostage crisis end time 18 August 1988, point in time 18 August 1988, country Germany, start time 16 August 1988.</code>	<code>Gladbeck hostage crisis country Netherlands, country Germany, participant Hans-Jürgen Rösner, participant Dieter Degowski.</code>
  <code>The album was originally released only on cassette tape before later being made available for digital download on iTunes and similar digital media stores.</code>	<code>Vongole Fisarmonica instance of Album.</code>	<code>Vongole Fisarmonica performer Those Darn Accordions, publication date 01 January 1992, instance of Album.</code>

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

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

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• learning_rate: 8e-05
• num_train_epochs: 1
• warmup_ratio: 0.05
• bf16: True
• batch_sampler: no_duplicates

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 512
• per_device_eval_batch_size: 512
• 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: 8e-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: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.05
• 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: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.49.0
• PyTorch: 2.6.0+cu124
• Accelerate: 1.5.2
• Datasets: 3.3.2
• 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",
}

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}
}

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