metadata

base_model: google-bert/bert-base-uncased
datasets:
  - sentence-transformers/gooaq
language:
  - en
library_name: sentence-transformers
license: apache-2.0
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
  - dot_accuracy@1
  - dot_accuracy@3
  - dot_accuracy@5
  - dot_accuracy@10
  - dot_precision@1
  - dot_precision@3
  - dot_precision@5
  - dot_precision@10
  - dot_recall@1
  - dot_recall@3
  - dot_recall@5
  - dot_recall@10
  - dot_ndcg@10
  - dot_mrr@10
  - dot_map@100
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:3002496
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: how to change date format in ms project 2007?
    sentences:
      - >-
        ['Choose File > Options.', 'Select General.', 'Under Project view, pick
        an option from the Date format list.']
      - >-
        Cats can be very affectionate and bonded with each other and still bond
        well and show affection to their human. Getting two kittens from the
        same litter, regardless of gender, can make it easier for them to
        befriend each other and play—but any two kittens generally tend to get
        on well after introductions.
      - >-
        Treat your permed hair like silk or another delicate fabric: washing it
        once a week is enough to keep it clean and help maintain its beauty.
        Wash your hair with warm water. Hot water can strip your hair of oils
        that help keep it moisturized and looking lustrous. Hot water can also
        ruin the curls.
  - source_sentence: is the mother in vinegar good for you?
    sentences:
      - >-
        Some people say the “mother,” the cloud of yeast and bacteria you might
        see in a bottle of apple cider vinegar, is what makes it healthy. These
        things are probiotic, meaning they might give your digestive system a
        boost, but there isn't enough research to back up the other claims.
      - >-
        It is normal for vaginal discharge to increase in amount and become
        “stringy” (like egg whites) during the middle of your menstrual cycle
        when you're ovulating. If you find that your normal discharge is
        annoying, you can wear panty liners/shields on your underwear.
      - >-
        State law protects cypress trees along Florida's waterways, but it has
        been up to the courts to enforce the regulations. ... Landowners can cut
        down cypress trees on their land, but trees below the high-water mark
        are considered state property and are protected.
  - source_sentence: if you're blocked on whatsapp can you see last seen?
    sentences:
      - >-
        Jaguars aren't going to London this year, releases new plan for season
        tickets. The Jaguars will no longer be playing two games in London, and
        will instead play both games at TIAA Bank Field.
      - >-
        Typically, most drugs are absorbed within 20-30 minutes after given by
        mouth. Vomiting after this amount of time is not related to the drug in
        the stomach as the vast majority, if not all, has already been absorbed.
      - >-
        You can no longer see a contact's last seen or online in the chat
        window. Learn more here. You do not see updates to a contact's profile
        photo. Any messages sent to a contact who has blocked you will always
        show one check mark (message sent), and never show a second check mark
        (message delivered).
  - source_sentence: how many enchantments can you put on armor?
    sentences:
      - >-
        4 Answers. You can in theory add every enchantment that is compatible
        with a tool/weapon/armor onto the same item. The bow can have these 7
        enchantments, though mending and infinity are mutually exclusive.
      - >-
        The sleeve length will make or break a jacket. If too long, it will make
        the jacket look too big, and if too short, like you have outgrown your
        jacket. ... This is when you need an experienced tailor, who will be
        able to shorten the sleeves from the shoulders, so the details on the
        cuffs are not disturbed.
      - >-
        Grace period of 60 days granted after the expiration of license for
        purpose of renewal, and license is valid during this period. Renewal of
        license may occur from 60 days (effective August 1, 2016, 180 days)
        prior to expiration to 3 years after date; afterwards, applicant
        required to take and pass examination.
  - source_sentence: what is the best drugstore shampoo for volume?
    sentences:
      - >-
        ['#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."]
      - >-
        Genes can't control an organism on their own; rather, they must interact
        with and respond to the organism's environment. Some genes are
        constitutive, or always "on," regardless of environmental conditions.
      - >-
        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.
co2_eq_emissions:
  emissions: 523.8395173647017
  energy_consumed: 1.3476635503925931
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 3.544
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: BERT base uncased trained on GooAQ triplets
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: gooaq dev
          type: gooaq-dev
        metrics:
          - type: cosine_accuracy@1
            value: 0.7001
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8712
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.9219
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9629
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.7001
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2904
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.18438000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09629000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7001
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8712
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9219
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9629
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8358567622290791
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7945682142857085
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.796615366916047
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.6709
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.8558
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.9096
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.9567
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.6709
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.28526666666666667
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.18192000000000003
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09567
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.6709
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.8558
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.9096
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.9567
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.8177950307933399
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.772776468253962
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.7751231358698718
            name: Dot Map@100

BERT base uncased trained on GooAQ triplets

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.

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

	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:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

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

	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:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

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

@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

@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}
}