This is a sentence-transformers model finetuned from nreimers/TinyBERT_L-4_H-312_v2. It maps sentences & paragraphs to a 312-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: nreimers/TinyBERT_L-4_H-312_v2
Maximum Sequence Length: 512 tokens
Output Dimensionality: 312 dimensions
Similarity Function: Cosine Similarity
Supported Modality: Text

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 312, 'pooling_mode': 'mean', '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/TinyBERT_L-4_H-312_v2-distilled-from-stsb-roberta-base-v2-projection-dim")
# Run inference
sentences = [
    'A black dog is drinking next to a brown and white dog that is looking at an orange ball in the lake, whilst a horse and rider passes behind.',
    'There are two people running around a track in lane three and the one wearing a blue shirt with a green thing over the eyes is just barely ahead of the guy wearing an orange shirt and sunglasses.',
    'the guy is dead',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 312]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.1955, -0.0013],
#         [ 0.1955,  1.0000, -0.0433],
#         [-0.0013, -0.0433,  1.0000]])

Evaluation

Metrics

Semantic Similarity

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

Metric	sts-dev	sts-test
pearson_cosine	0.808	0.7473
spearman_cosine	0.8203	0.7517

Training Details

Training Dataset

Unnamed Dataset

Size: 200,000 training samples
Columns: sentence and label
Approximate statistics based on the first 100 samples:
sentence label
type string list
modality text
details
min: 8 tokens
mean: 17.05 tokens
max: 52 tokens
size: 768 elements

Samples:

sentence	label
`A person on a horse jumps over a broken down airplane.`	`[-0.477225124835968, -0.027898235246539116, 0.6169318556785583, -1.6224359273910522, 0.7474681735038757, ...]`
`Children smiling and waving at camera`	`[-0.1697935163974762, 0.9077808856964111, -0.8368250727653503, -0.47047966718673706, -0.5604732036590576, ...]`
`A boy is jumping on skateboard in the middle of a red bridge.`	`[0.6267533898353577, 0.011438215151429176, 0.47103747725486755, 0.4887479841709137, -0.3095979690551758, ...]`

Loss: MSELoss with these parameters:
json
1{ 2 "projection_dim": 768 3}

Evaluation Dataset

Unnamed Dataset

Size: 10,000 evaluation samples
Columns: sentence and label
Approximate statistics based on the first 100 samples:
sentence label
type string list
modality text
details
min: 7 tokens
mean: 17.97 tokens
max: 45 tokens
size: 768 elements

Samples:

sentence	label
`Two women are embracing while holding to go packages.`	`[1.3980050086975098, 0.659657895565033, -0.671194851398468, -0.3568831980228424, 0.08937378972768784, ...]`
`Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.`	`[0.08953701704740524, -0.16486810147762299, -0.5275247097015381, -0.13387243449687958, 0.3173069953918457, ...]`
`A man selling donuts to a customer during a world exhibition event held in the city of Angeles`	`[-0.18134362995624542, -0.27244624495506287, 0.6053312420845032, 0.4879472851753235, -0.4728725850582123, ...]`

Loss: MSELoss with these parameters:
json
1{ 2 "projection_dim": 768 3}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
num_train_epochs: 1
learning_rate: 0.0001
warmup_steps: 0.1
fp16: True
per_device_eval_batch_size: 64
load_best_model_at_end: True

All Hyperparameters

Click to expand

per_device_train_batch_size: 64
num_train_epochs: 1
max_steps: -1
learning_rate: 0.0001
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0.1
optim: adamw_torch_fused
optim_args: None
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optim_target_modules: None
gradient_accumulation_steps: 1
average_tokens_across_devices: True
max_grad_norm: 1.0
label_smoothing_factor: 0.0
bf16: False
fp16: True
bf16_full_eval: False
fp16_full_eval: False
tf32: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
use_liger_kernel: False
liger_kernel_config: None
use_cache: False
neftune_noise_alpha: None
torch_empty_cache_steps: None
auto_find_batch_size: False
log_on_each_node: True
logging_nan_inf_filter: True
include_num_input_tokens_seen: no
log_level: passive
log_level_replica: warning
disable_tqdm: False
project: huggingface
trackio_space_id: None
trackio_bucket_id: None
trackio_static_space_id: None
per_device_eval_batch_size: 64
prediction_loss_only: True
eval_on_start: False
eval_do_concat_batches: True
eval_use_gather_object: False
eval_accumulation_steps: None
include_for_metrics: []
batch_eval_metrics: False
save_only_model: False
save_on_each_node: False
enable_jit_checkpoint: False
push_to_hub: False
hub_private_repo: None
hub_model_id: None
hub_strategy: every_save
hub_always_push: False
hub_revision: None
load_best_model_at_end: True
ignore_data_skip: False
restore_callback_states_from_checkpoint: False
full_determinism: False
seed: 42
data_seed: None
use_cpu: False
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_pin_memory: True
dataloader_persistent_workers: False
dataloader_prefetch_factor: None
remove_unused_columns: True
label_names: None
train_sampling_strategy: random
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
ddp_static_graph: None
ddp_backend: None
ddp_timeout: 1800
fsdp: []
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
deepspeed: None
debug: []
skip_memory_metrics: True
do_predict: False
resume_from_checkpoint: None
warmup_ratio: None
local_rank: -1
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
0.032	100	0.3832	-	-	-
0.064	200	0.3556	-	-	-
0.096	300	0.3163	-	-	-
0.128	400	0.2814	-	-	-
0.16	500	0.2573	0.2855	0.7637	-
0.192	600	0.2412	-	-	-
0.224	700	0.2285	-	-	-
0.256	800	0.2205	-	-	-
0.288	900	0.2112	-	-	-
0.32	1000	0.2038	0.2533	0.7988	-
0.352	1100	0.1980	-	-	-
0.384	1200	0.1932	-	-	-
0.416	1300	0.1889	-	-	-
0.448	1400	0.1853	-	-	-
0.48	1500	0.1835	0.2375	0.8114	-
0.512	1600	0.1780	-	-	-
0.544	1700	0.1765	-	-	-
0.576	1800	0.1741	-	-	-
0.608	1900	0.1714	-	-	-
0.64	2000	0.1696	0.2292	0.8153	-
0.672	2100	0.1685	-	-	-
0.704	2200	0.1677	-	-	-
0.736	2300	0.1663	-	-	-
0.768	2400	0.1642	-	-	-
0.8	2500	0.1629	0.2246	0.8187	-
0.832	2600	0.1615	-	-	-
0.864	2700	0.1616	-	-	-
0.896	2800	0.1606	-	-	-
0.928	2900	0.1603	-	-	-
0.96	3000	0.1603	0.2217	0.8196	-
0.992	3100	0.1591	-	-	-
1.0	3125	-	0.2215	0.8203	-
-1	-1	-	-	-	0.7517

The bold row denotes the saved checkpoint.

Training Time

Training: 4.1 minutes
Evaluation: 1.2 minutes
Total: 5.3 minutes

Framework Versions

Python: 3.11.6
Sentence Transformers: 5.5.0.dev0
Transformers: 5.6.2
PyTorch: 2.10.0+cu128
Accelerate: 1.13.0.dev0
Datasets: 4.8.4
Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

bibtex

@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",
}

MSELoss

bibtex

@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}

	sentence	label
type	string	list
modality	text
details	min: 8 tokens mean: 17.05 tokens max: 52 tokens	size: 768 elements