SentenceTransformer based on google/bert_uncased_L-2_H-128_A-2

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.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("swardiantara/bert-tiny-amazon_reviews-k3-fixed-euclidean")
# Run inference
sentences = [
    'Only worked for about a month and now dead?',
    "The blades have come apart. Very hard to use. I guess it's more for commercial use and bolted to a table.",
    "I don't know - for some reason I was disappointed in this product. It was as advertised but the pictures are so small and not put into a work out routine - seems scattered. Would have returned them but repackaging them would have been a pain.",
]
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, 0.9976, 0.9982],
#         [0.9976, 1.0000, 0.9971],
#         [0.9982, 0.9971, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 2,600,030 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: 11 tokens mean: 41.25 tokens max: 128 tokens	min: 17 tokens mean: 52.32 tokens max: 101 tokens	size: 2 elements

Samples:

text_a	text_b	label
Arrived broken. Manufacturer defect. Two of the legs of the base were not completely formed, so there was no way to insert the casters. I unpackaged the entire chair and hardware before noticing this. So, I'll spend twice the amount of time boxing up the whole useless thing and send it back with a 1-star review of part of a chair I never got to sit in. I will go so far as to include a picture of what their injection molding and quality assurance process missed though. I will be hesitant to buy again. It makes me wonder if there aren't missing structures and supports that don't impede the assembly process.	`I ordered two of these. One came broken. It was packaged okay, so I believe it was broken due to the mail carrier tossing it onto my porch. The one that did not break, I love. It looks nice on my counter and serves its purpose. Updated review: Ok so the salt cellar that did not break during shipping pretty much shattered at the lightest touch after 3 weeks on my counter. I can no longer recommend this item, clearly they are not very durable at all.`	`[1.0, 0.0]`
Arrived broken. Manufacturer defect. Two of the legs of the base were not completely formed, so there was no way to insert the casters. I unpackaged the entire chair and hardware before noticing this. So, I'll spend twice the amount of time boxing up the whole useless thing and send it back with a 1-star review of part of a chair I never got to sit in. I will go so far as to include a picture of what their injection molding and quality assurance process missed though. I will be hesitant to buy again. It makes me wonder if there aren't missing structures and supports that don't impede the assembly process.	`Seems to protect well, feels nice, just wish it would stick a bit better. The first one would not stay applied. Kept peeling from the top no matter what I did. I figured it could be my error, so I removed and cleaned the screen very well with additional alcohol wipes. The second one has been on a few days, but is starting to peel from a different spot. I love the feel of the protector otherwise.`	`[0.0, 0.25]`
Arrived broken. Manufacturer defect. Two of the legs of the base were not completely formed, so there was no way to insert the casters. I unpackaged the entire chair and hardware before noticing this. So, I'll spend twice the amount of time boxing up the whole useless thing and send it back with a 1-star review of part of a chair I never got to sit in. I will go so far as to include a picture of what their injection molding and quality assurance process missed though. I will be hesitant to buy again. It makes me wonder if there aren't missing structures and supports that don't impede the assembly process.	`I ended up returning this bag for credit. While there was nothing wrong with the quality, it turned out to be too small.`	`[0.0, 0.25]`

Loss: main.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
0.1969	500	0.4126
0.3937	1000	0.0819
0.5906	1500	0.0759
0.7874	2000	0.0717
0.9843	2500	0.0697
1.0	2540	-
1.1811	3000	0.0684
1.3780	3500	0.0666
1.5748	4000	0.0658
1.7717	4500	0.0651
1.9685	5000	0.0644
2.0	5080	-
2.1654	5500	0.0643
2.3622	6000	0.0639
2.5591	6500	0.0635
2.7559	7000	0.0631
2.9528	7500	0.0634
3.0	7620	-

The bold row denotes the saved checkpoint.

Training Time

Training: 26.6 minutes
Evaluation: 1.3 seconds
Total: 26.6 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

@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",
}

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Safetensors

Model size

4.39M params

Tensor type

F32

Model tree for swardiantara/bert-tiny-amazon_reviews-k3-fixed-euclidean

Base model

google/bert_uncased_L-2_H-128_A-2

Finetuned

(120)

this model

Paper for swardiantara/bert-tiny-amazon_reviews-k3-fixed-euclidean

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 15