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-k5-fixed-euclidean")
# Run inference
sentences = [
    'Wrong size for my husband. I need to return them, unfortunately.',
    "Loved this hair brush! It's now much easier to detangle my frizzy hair after I wake up in the mornings. I wish I had found out about this much earlier. Thank you!",
    'These hooks work great for my boutique I have. I am able to hang scarves and different things from them including purses. They take up less space and last forever. The quality is excellent and so is the price!',
]
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, 0.9983, 0.9977],
#         [0.9983, 1.0000, 0.9985],
#         [0.9977, 0.9985, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 4,200,100 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: 15 tokens mean: 51.66 tokens max: 128 tokens	min: 17 tokens mean: 59.45 tokens max: 106 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 this a while ago, even using prime it took over a month to get here, now that I finally got it, I only receved 6 of the 12! Im a little upset. It's still funny, but would not recomend for someone who needs foam quick and in bulk like i do.`	`[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.	`Had this Item for a year and it is already ripping everywhere and faded badly. I do not recommend.`	`[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 was so disappointed in this mascara. I really like Stila Products too, but it was just a clumpy mess on brush and hard to apply with the wand design. I had to be extra careful even just applying it because of how messy it was coming out of tube. Ended up returning it because Amazon is super great on their return policy on Prime items.`	`[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
0.1219	500	0.2609
0.2438	1000	0.0550
0.3657	1500	0.0503
0.4876	2000	0.0470
0.6095	2500	0.0458
0.7314	3000	0.0449
0.8532	3500	0.0440
0.9751	4000	0.0431
1.0	4102	-
1.0970	4500	0.0425
1.2189	5000	0.0422
1.3408	5500	0.0417
1.4627	6000	0.0411
1.5846	6500	0.0408
1.7065	7000	0.0405
1.8284	7500	0.0405
1.9503	8000	0.0405
2.0	8204	-
2.0722	8500	0.0402
2.1941	9000	0.0401
2.3159	9500	0.0400
2.4378	10000	0.0399
2.5597	10500	0.0395
2.6816	11000	0.0396
2.8035	11500	0.0396
2.9254	12000	0.0398
3.0	12306	-

The bold row denotes the saved checkpoint.

Training Time

Training: 43.9 minutes
Evaluation: 1.3 seconds
Total: 43.9 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-k5-fixed-euclidean

Base model

google/bert_uncased_L-2_H-128_A-2

Finetuned

(120)

this model

Paper for swardiantara/bert-tiny-amazon_reviews-k5-fixed-euclidean

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 15