SentenceTransformer (Legacy)

UPDATE: Consider using johnnyboycurtis/ModernBERT-small-v2 a much more performant model.

Warning

This model was an early exploration into creating a Wide model.

⚠️ Legacy Status: NOT RECOMMENDED.

This initial implementation suffered from suboptimal architectural scaling decisions made during the initialization phase, particularly concerning the feed-forward network capacity relative to the depth.

👉 Recommended Successor: For superior performance, speed, and architectural coherence, please use the improved version: johnnyboycurtis/ModernBERT-small-v2. The successor model addresses these limitations via a more sophisticated Guided Weight Initialization (GUIDE) technique and specialized Knowledge Distillation tuning.

Model Details

This is a shallow model with wide layers. NOT RECOMMENDED for production. This was my first attempt of training a ModernBERT model from scratch. The wide component is a mistake on my part due to lack of understanding of the gegelu design.

ModernBERT-small-1.5 will address the limitations of this design.

small_modernbert_config = ModernBertConfig(
 hidden_size=384, # A common dimension for small embedding models
 num_hidden_layers=12, # Significantly fewer layers than the base's 22
 num_attention_heads=6, # Must be a divisor of hidden_size
 intermediate_size=1536, # 4 * hidden_size -- VERY WIDE!!
 max_position_embeddings=1024, # Max sequence length for the model; originally 8192
)

model = ModernBertModel(modernbert_small_config)

Model Description

• Model Type: Sentence Transformer
• Base model: Custom-trained ModernBERT-Small (trained from scratch)
• Architecture: ModernBERT-Small
• Maximum Sequence Length: 1024 tokens
• Output Dimensionality: 384 dimensions
• Similarity Function: Cosine Similarity
• Language: en
• License: MIT

Model Sources

• Repository: ModernBERT Training Scripts
• Documentation: Sentence Transformers Documentation
• Hugging Face: Sentence Transformers on Hugging Face

Training Procedure

This model was developed using a sophisticated, multi-stage "curriculum learning" approach to build a deep semantic understanding. The training scripts are available in the linked repository.

Stage 1: Foundational Contrastive Training

The model was first trained on a large, diverse collection of over 1 million triplets from three different datasets. This stage taught the model a broad, foundational understanding of language, relevance, and logical relationships.

• Datasets:
  • sentence-transformers/all-nli
  • sentence-transformers/trivia-qa-triplet
  • sentence-transformers/msmarco-msmarco-distilbert-base-v3
• Loss Function: MultipleNegativesRankingLoss

Stage 2: Advanced Knowledge Distillation

The foundational model was then refined by having it mimic a state-of-the-art teacher model (BAAI/bge-base-en-v1.5). This stage transferred the nuanced knowledge of the expert teacher to our more efficient student model.

• Teacher Model: BAAI/bge-base-en-v1.5
• Loss Function: DistillKLDivLoss

Stage 3: Task-Specific Fine-Tuning

As a final "calibration" step, the best distilled model was fine-tuned directly on the Semantic Textual Similarity (STS) benchmark. This specializes the model for tasks requiring precise similarity scores.

• Dataset: sentence-transformers/stsb
• Loss Function: CosineSimilarityLoss

Full Model Architecture

SentenceTransformer(
 (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False, 'architecture': 'ModernBertModel'})
 (1): Pooling({'word_embedding_dimension': 384, '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("johnnyboycurtis/ModernBERT-small")
# Run inference
sentences = [
 'A girl is eating a cupcake.',
 'A woman is eating a cupcake.',
 'Zebras are socializing.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8201, 0.1449],
# [0.8201, 1.0000, 0.1839],
# [0.1449, 0.1839, 1.0000]])

Evaluation

Metrics

Semantic Similarity

• Datasets: sts-dev and sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Training Details

Training Dataset

stsb

• Dataset: stsb at ab7a5ac
• 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: 6 tokens mean: 10.16 tokens max: 28 tokens	min: 6 tokens mean: 10.12 tokens max: 25 tokens	min: 0.0 mean: 0.45 max: 1.0

• Samples:

  sentence1	sentence2	score
  A plane is taking off.	An air plane is taking off.	1.0
  A man is playing a large flute.	A man is playing a flute.	0.76
  A man is spreading shreded cheese on a pizza.	A man is spreading shredded cheese on an uncooked pizza.	0.76

• Loss: CosineSimilarityLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• num_train_epochs: 4
• warmup_ratio: 0.1
• bf16: True
• load_best_model_at_end: True

All Hyperparameters

Training Logs

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.13.4
• Sentence Transformers: 5.0.0
• Transformers: 4.52.4
• PyTorch: 2.7.1+cu128
• Accelerate: 1.7.0
• 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",
}