This is a sentence-transformers model finetuned from google/bert_uncased_L-2_H-128_A-2. It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: google/bert_uncased_L-2_H-128_A-2
Maximum Sequence Length: 128 tokens
Output Dimensionality: 128 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': 128, '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("swardiantara/bert-tiny-snli-k5-adaptive-euclidean")
# Run inference
sentences = [
    'Children smiling and waving at camera [SEP] The kids are frowning',
    'A room full of girls raising their hands. [SEP] The boys are jumping on the trampoline.',
    'An older man in a red vest and blue cap is walking with a cane carrying a white bag. [SEP] The man is wearing green shorts.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 128]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9973, 0.9960],
#         [0.9973, 1.0000, 0.9924],
#         [0.9960, 0.9924, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 6,043,097 training samples
Columns: text_a, text_b, and label
Approximate statistics based on the first 100 samples:
text_a text_b label
type string string list
modality text text
details
min: 13 tokens
mean: 22.88 tokens
max: 47 tokens
min: 20 tokens
mean: 27.54 tokens
max: 43 tokens
size: 2 elements

Samples:

text_a	text_b	label
`A person on a horse jumps over a broken down airplane. [SEP] A person is training his horse for a competition.`	`A woman is walking across the street eating a banana, while a man is following with his briefcase. [SEP] the woman is a seductress`	`[1.0, 0.0]`
`A person on a horse jumps over a broken down airplane. [SEP] A person is training his horse for a competition.`	`A man on stilts is playing a tuba for money on the boardwalk. [SEP] A male street performer with a tuba is playing outside.`	`[0.0, 0.5]`
`A person on a horse jumps over a broken down airplane. [SEP] A person is training his horse for a competition.`	`A boy is standing next to a car in front of a clothesline. [SEP] The boy is outside.`	`[0.0, 0.5]`

Loss: [object Object].OrdinalProxyContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 1024
learning_rate: 1e-05
load_best_model_at_end: True

All Hyperparameters

Click to expand

per_device_train_batch_size: 1024
num_train_epochs: 3
max_steps: -1
learning_rate: 1e-05
lr_scheduler_type: linear
lr_scheduler_kwargs: None
warmup_steps: 0
optim: adamw_torch
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: False
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: 8
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: None
fsdp_config: None
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
0.0847	500	0.4805
0.1694	1000	0.1181
0.2542	1500	0.1074
0.3389	2000	0.1024
0.4236	2500	0.0982
0.5083	3000	0.0951
0.5930	3500	0.0939
0.6777	4000	0.0922
0.7625	4500	0.0918
0.8472	5000	0.0912
0.9319	5500	0.0898
1.0	5902	-
1.0166	6000	0.0897
1.1013	6500	0.0891
1.1860	7000	0.0886
1.2708	7500	0.0879
1.3555	8000	0.0883
1.4402	8500	0.0874
1.5249	9000	0.0874
1.6096	9500	0.0867
1.6943	10000	0.0869
1.7791	10500	0.0868
1.8638	11000	0.0860
1.9485	11500	0.0851
2.0	11804	-
2.0332	12000	0.0848
2.1179	12500	0.0837
2.2026	13000	0.0833
2.2874	13500	0.0831
2.3721	14000	0.0817
2.4568	14500	0.0819
2.5415	15000	0.0812
2.6262	15500	0.0812
2.7109	16000	0.0807
2.7957	16500	0.0807
2.8804	17000	0.0801
2.9651	17500	0.0798
3.0	17706	-

The bold row denotes the saved checkpoint.

Training Time

Training: 36.5 minutes
Evaluation: 2.7 seconds
Total: 36.5 minutes

Framework Versions

Python: 3.12.4
Sentence Transformers: 5.5.1
Transformers: 5.11.0
PyTorch: 2.5.1+cu121
Accelerate: 1.13.0
Datasets: 2.21.0
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",
}

	text_a	text_b	label
type	string	string	list
modality	text	text
details	min: 13 tokens mean: 22.88 tokens max: 47 tokens	min: 20 tokens mean: 27.54 tokens max: 43 tokens	size: 2 elements