SentenceTransformer

This is a sentence-transformers model based on a custom ModernBERT-Small architecture, trained from scratch using a multi-stage pipeline. It maps sentences & paragraphs to a 384-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: 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:
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

Metric	sts-dev	sts-test
pearson_cosine	0.7575	0.6939
spearman_cosine	0.7563	0.6784

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

	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

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
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: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 2e-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: 4
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: 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: True
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
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
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
0.2778	100	0.1535	-	-
0.5556	200	0.068	0.7387	-
0.8333	300	0.0446	-	-
1.1111	400	0.0411	0.7511	-
1.3889	500	0.0366	-	-
1.6667	600	0.0425	0.7542	-
1.9444	700	0.0402	-	-
2.2222	800	0.0373	0.7563	-
2.5	900	0.0374	-	-
2.7778	1000	0.0384	0.7557	-
3.0556	1100	0.0357	-	-
3.3333	1200	0.0399	0.7562	-
3.6111	1300	0.0358	-	-
3.8889	1400	0.0338	0.7563	-
-1	-1	-	-	0.6784

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",
}

johnnyboycurtis
/

ModernBERT-small