CocoRoF / ModernBERT-SimCSE-multitask_v03-retry

SentenceTransformer based on CocoRoF/mobert_retry_SimCSE_test

This is a sentence-transformers model finetuned from CocoRoF/mobert_retry_SimCSE_test . 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: CocoRoF/mobert_retry_SimCSE_test
• Maximum Sequence Length: 2048 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': 2048, '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): Dense({'in_features': 768, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

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("CocoRoF/ModernBERT-SimCSE-multitask_v03-retry")
# Run inference
sentences = [
    '버스가 바쁜 길을 따라 운전한다.',
    '녹색 버스가 도로를 따라 내려간다.',
    '그 여자는 데이트하러 가는 중이다.',
]
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]

Evaluation

Metrics

Semantic Similarity

• Dataset: sts_dev
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

Unnamed Dataset

• Size: 5,749 training samples
• Columns: sentence1 , sentence2 , and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 7 tokens mean: 13.52 tokens max: 36 tokens	min: 7 tokens mean: 13.41 tokens max: 32 tokens	min: 0.0 mean: 0.45 max: 1.0

• Samples:

  sentence1	sentence2	score
  비행기가 이륙하고 있다.	비행기가 이륙하고 있다.	1.0
  한 남자가 큰 플루트를 연주하고 있다.	남자가 플루트를 연주하고 있다.	0.76
  한 남자가 피자에 치즈를 뿌려놓고 있다.	한 남자가 구운 피자에 치즈 조각을 뿌려놓고 있다.	0.76

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 1,500 evaluation samples
• Columns: sentence1 , sentence2 , and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 7 tokens mean: 20.38 tokens max: 52 tokens	min: 6 tokens mean: 20.52 tokens max: 54 tokens	min: 0.0 mean: 0.42 max: 1.0

• Samples:

  sentence1	sentence2	score
  안전모를 가진 한 남자가 춤을 추고 있다.	안전모를 쓴 한 남자가 춤을 추고 있다.	1.0
  어린아이가 말을 타고 있다.	아이가 말을 타고 있다.	0.95
  한 남자가 뱀에게 쥐를 먹이고 있다.	남자가 뱀에게 쥐를 먹이고 있다.	1.0

• Loss: CosineSimilarityLoss with these parameters:

  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• overwrite_output_dir : True
• eval_strategy : steps
• per_device_train_batch_size : 1
• per_device_eval_batch_size : 1
• gradient_accumulation_steps : 16
• learning_rate : 8e-05
• num_train_epochs : 10.0
• warmup_ratio : 0.2
• push_to_hub : True
• hub_model_id : CocoRoF/ModernBERT-SimCSE-multitask_v03-retry
• hub_strategy : checkpoint
• batch_sampler : no_duplicates

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.11.10
• Sentence Transformers: 3.4.1
• Transformers: 4.48.3
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.3.0
• 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",
}