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BERT base uncased trained on GooAQ triplets

Name: bert-base-uncased-gooaq-og
Availability: InStock

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased on the sentence-transformers/gooaq dataset. It maps sentences & paragraphs to a 768-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: google-bert/bert-base-uncased
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- sentence-transformers/gooaq
Language: en
License: apache-2.0

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/bert-base-uncased-gooaq")
# Run inference
sentences = [
    'what is the best drugstore shampoo for volume?',
    '[\'#8. ... \', \'#7. ... \', \'#6. Hask Biotin Boost Shampoo. ... \', \'#5. Pantene Pro-V Sheer Volume Shampoo. ... \', \'#4. John Frieda Luxurious Volume Touchably Full Shampoo. ... \', \'#3. Acure Vivacious Volume Peppermint Shampoo. ... \', \'#2. OGX Thick & Full Biotin & Collagen Shampoo. ... \', "#1. L\'Oréal Paris EverPure Sulfate Free Volume Shampoo."]',
    'In electricity, the phase refers to the distribution of a load. What is the difference between single-phase and three-phase power supplies? Single-phase power is a two-wire alternating current (ac) power circuit. ... Three-phase power is a three-wire ac power circuit with each phase ac signal 120 electrical degrees apart.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Dataset: gooaq-dev
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7001
cosine_accuracy@3	0.8712
cosine_accuracy@5	0.9219
cosine_accuracy@10	0.9629
cosine_precision@1	0.7001
cosine_precision@3	0.2904
cosine_precision@5	0.1844
cosine_precision@10	0.0963
cosine_recall@1	0.7001
cosine_recall@3	0.8712
cosine_recall@5	0.9219
cosine_recall@10	0.9629
cosine_ndcg@10	0.8359
cosine_mrr@10	0.7946
cosine_map@100	0.7966
dot_accuracy@1	0.6709
dot_accuracy@3	0.8558
dot_accuracy@5	0.9096
dot_accuracy@10	0.9567
dot_precision@1	0.6709
dot_precision@3	0.2853
dot_precision@5	0.1819
dot_precision@10	0.0957
dot_recall@1	0.6709
dot_recall@3	0.8558
dot_recall@5	0.9096
dot_recall@10	0.9567
dot_ndcg@10	0.8178
dot_mrr@10	0.7728
dot_map@100	0.7751

Training Details

Training Dataset

sentence-transformers/gooaq

Dataset: sentence-transformers/gooaq at b089f72
Size: 3,002,496 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 11.95 tokens
max: 24 tokens
min: 17 tokens
mean: 60.83 tokens
max: 130 tokens

Samples:

question	answer
`what are the differences between internet and web?`	`The Internet is a global network of networks while the Web, also referred formally as World Wide Web (www) is collection of information which is accessed via the Internet. Another way to look at this difference is; the Internet is infrastructure while the Web is service on top of that infrastructure.`
`who is the most important person in a first aid situation?`	`Subscribe to New First Aid For Free The main principle of incident management is that you are the most important person and your safety comes first! Your first actions when coming across the scene of an incident should be: Check for any dangers to yourself or bystanders. Manage any dangers found (if safe to do so)`
`why is jibjab not working?`	`Usually disabling your ad blockers for JibJab will resolve this issue. If you're still having issues loading the card after your ad blockers are disabled, you can try clearing your cache/cookies or updating and restarting your browser. As a last resort, you can try opening JibJab from a different browser.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Evaluation Dataset

sentence-transformers/gooaq

Dataset: sentence-transformers/gooaq at b089f72
Size: 10,000 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 8 tokens
mean: 12.01 tokens
max: 34 tokens
min: 13 tokens
mean: 59.81 tokens
max: 145 tokens

Samples:

question	answer
`what are some common attributes/characteristics between animal and human?`	`['Culture.', 'Emotions.', 'Language.', 'Humour.', 'Tool Use.', 'Memory.', 'Self-Awareness.', 'Intelligence.']`
`is folic acid the same as vitamin b?`	`Vitamin B9, also called folate or folic acid, is one of 8 B vitamins. All B vitamins help the body convert food (carbohydrates) into fuel (glucose), which is used to produce energy. These B vitamins, often referred to as B-complex vitamins, also help the body use fats and protein.`
`are bendy buses still in london?`	`Bendy bus makes final journey for Transport for London. The last of London's bendy buses was taken off the roads on Friday night. ... The final route to be operated with bendy buses has been the 207 between Hayes and White City, and the last of the long vehicles was to run late on Friday.`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim" 4}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 2e-05
num_train_epochs: 1
warmup_ratio: 0.1
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_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: 1
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: False
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: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
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
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	gooaq-dev_cosine_map@100
0	0	-	-	0.2018
0.0000	1	2.6207	-	-
0.0213	500	0.9092	-	-
0.0426	1000	0.2051	-	-
0.0639	1500	0.1354	-	-
0.0853	2000	0.1089	0.0719	0.7124
0.1066	2500	0.0916	-	-
0.1279	3000	0.0812	-	-
0.1492	3500	0.0716	-	-
0.1705	4000	0.0658	0.0517	0.7432
0.1918	4500	0.0623	-	-
0.2132	5000	0.0596	-	-
0.2345	5500	0.0554	-	-
0.2558	6000	0.0504	0.0401	0.7580
0.2771	6500	0.0498	-	-
0.2984	7000	0.0483	-	-
0.3197	7500	0.0487	-	-
0.3410	8000	0.0458	0.0359	0.7652
0.3624	8500	0.0435	-	-
0.3837	9000	0.0421	-	-
0.4050	9500	0.0421	-	-
0.4263	10000	0.0405	0.0329	0.7738
0.4476	10500	0.0392	-	-
0.4689	11000	0.0388	-	-
0.4903	11500	0.0388	-	-
0.5116	12000	0.0361	0.0290	0.7810
0.5329	12500	0.0362	-	-
0.5542	13000	0.0356	-	-
0.5755	13500	0.0352	-	-
0.5968	14000	0.0349	0.0267	0.7866
0.6182	14500	0.0334	-	-
0.6395	15000	0.0323	-	-
0.6608	15500	0.0325	-	-
0.6821	16000	0.0316	0.0256	0.7879
0.7034	16500	0.0313	-	-
0.7247	17000	0.0306	-	-
0.7460	17500	0.0328	-	-
0.7674	18000	0.0303	0.0238	0.7928
0.7887	18500	0.0301	-	-
0.8100	19000	0.0291	-	-
0.8313	19500	0.0286	-	-
0.8526	20000	0.0295	0.0218	0.7952
0.8739	20500	0.0288	-	-
0.8953	21000	0.0277	-	-
0.9166	21500	0.0266	-	-
0.9379	22000	0.0289	0.0218	0.7971
0.9592	22500	0.0286	-	-
0.9805	23000	0.0275	-	-
1.0	23457	-	-	0.7966

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 1.348 kWh
Carbon Emitted: 0.524 kg of CO2
Hours Used: 3.544 hours

Training Hardware

On Cloud: No
GPU Model: 1 x NVIDIA GeForce RTX 3090
CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
RAM Size: 31.78 GB

Framework Versions

Python: 3.11.6
Sentence Transformers: 3.1.0.dev0
Transformers: 4.41.2
PyTorch: 2.3.0+cu121
Accelerate: 0.31.0
Datasets: 2.20.0
Tokenizers: 0.19.1

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",
}

MultipleNegativesRankingLoss

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	question	answer
type	string	string
details	min: 8 tokens mean: 11.95 tokens max: 24 tokens	min: 17 tokens mean: 60.83 tokens max: 130 tokens

tomaarsen/bert-base-uncased-gooaq-og

BERT base uncased trained on GooAQ triplets

Model Details

Model Description

Model Sources

Full Model Architecture

Usage

Direct Usage (Sentence Transformers)

Evaluation

Metrics

Information Retrieval

Training Details

Training Dataset

sentence-transformers/gooaq

Evaluation Dataset

sentence-transformers/gooaq

Training Hyperparameters

Non-Default Hyperparameters

All Hyperparameters

Training Logs

Environmental Impact

Training Hardware

Framework Versions

Citation

BibTeX

Sentence Transformers

MultipleNegativesRankingLoss

tomaarsen/
bert-base-uncased-gooaq-og