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: extreme old age is called?
    sentences:
      - >-
        The organic process of ageing is called senescence, the medical study of
        the aging process is called gerontology, and the study of diseases that
        afflict the elderly is called geriatrics. ... Old age is not a definite
        biological stage, as the chronological age denoted as "old age" varies
        culturally and historically.
      - >-
        The syllabus is described as the summary of the topics covered or units
        to be taught in the particular subject. Curriculum refers to the overall
        content, taught in an educational system or a course. ... Syllabus is
        descriptive in nature, but the curriculum is prescriptive. Syllabus is
        set for a particular subject.
      - >-
        Keep records for 3 years from the date you filed your original return or
        2 years from the date you paid the tax, whichever is later, if you file
        a claim for credit or refund after you file your return. Keep records
        for 7 years if you file a claim for a loss from worthless securities or
        bad debt deduction.
  - source_sentence: has or as when to use?
    sentences:
      - >-
        Re: Has or as As is an adverb used in comparisons to refer to the extent
        or degree of something; a conjunction 1 used to indicate simultaneous
        occurrence. 2 used to indicate by comparison the way that something
        happens.
      - >-
        Go through their posts, likes, comments, and followers to see if the
        suspect's username appears. If the user's name appears, click on it. If
        you click on the user's profile and are unable to see their content,
        even though it says they have a number of posts at the top of their
        profile, then they have blocked you.
      - >-
        There's just a 2.6% + $0.30 fee on any portion funded by your credit or
        debit card.
  - source_sentence: how many inches of snow is good for snowboarding?
    sentences:
      - >-
        All kinds of tomato paste come with a best-by date. Like other
        condiments, such as bbq sauce, the unopened paste will easily last
        months past the date on the label.
      - >-
        Data Storage Data in an SD card is stored on a series of electronic
        components called NAND chips. These chips allow data to be written and
        stored on the SD card. As the chips have no moving parts, data can be
        transferred from the cards quickly, far exceeding the speeds available
        to CD or hard-drive media.
      - >-
        In these areas, as little as 2-4 inches of snow may be sufficient. Other
        pistes, however, may traverse uneven, rocky terrain. In these areas,
        several inches to several feet may be necessary to cover the rocky
        surface. Even more important than the amount of snowfall is the amount
        of snow that is retained on the slopes.
  - source_sentence: is it normal to have a period after not having one for 8 months?
    sentences:
      - >-
        It is not normal to bleed or spot 12 months or more after your last
        period. Bleeding after menopause is usually a sign of a minor health
        problem but can sometimes be an early sign of more serious disease.
      - >-
        ['What are your recruiting needs for my class? ... ', 'What are the next
        steps in the recruiting process with your program? ... ', 'What is your
        recruiting timeline? ... ', 'What does a typical day or week look like
        for a player during the season? ... ', 'What are the off-season
        expectations for a player? ... ', 'What are the values of your
        program?']
      - >-
        Registered retirement savings plans (RRSP) and registered pension plans
        (RPP) are both retirement savings plans that are registered with the
        Canada Revenue Agency (CRA). RRSPs are individual retirement plans,
        while RPPs are plans established by companies to provide pensions to
        their employees.
  - source_sentence: what health services are covered by medicare?
    sentences:
      - >-
        Medicare Part A hospital insurance covers inpatient hospital care,
        skilled nursing facility, hospice, lab tests, surgery, home health care.
      - >-
        Meiocytes are the diploid cells which undergo meiosis to produce
        gametes. They are also known as gamete mother cells. The chromosome
        number in diploid cells of onion is 16. So meiocytes have 16
        chromosomes.
      - >-
        Elephants have the longest gestation period of all mammals. These gentle
        giants' pregnancies last for more than a year and a half. The average
        gestation period of an elephant is about 640 to 660 days, or roughly 95
        weeks.
co2_eq_emissions:
  emissions: 408.66249919578786
  energy_consumed: 1.0513516760803594
  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: 2.832
  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: 0.576
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7295
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7824
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8462
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.576
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.24316666666666664
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.15648
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08462
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.576
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7295
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.7824
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8462
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7089171465159466
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6652589285714262
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6708962490161547
            name: Cosine Map@100
          - type: dot_accuracy@1
            value: 0.5263
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6922
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.7494
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8175
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.5263
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.23073333333333335
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14987999999999999
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08175
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.5263
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.6922
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.7494
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.8175
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6696727448603579
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.622603690476188
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.6291100061102131
            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: PeftModelForFeatureExtraction 
  (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-peft")
# Run inference
sentences = [
    'what health services are covered by medicare?',
    'Medicare Part A hospital insurance covers inpatient hospital care, skilled nursing facility, hospice, lab tests, surgery, home health care.',
    "Elephants have the longest gestation period of all mammals. These gentle giants' pregnancies last for more than a year and a half. The average gestation period of an elephant is about 640 to 660 days, or roughly 95 weeks.",
]
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	0.576
cosine_accuracy@3	0.7295
cosine_accuracy@5	0.7824
cosine_accuracy@10	0.8462
cosine_precision@1	0.576
cosine_precision@3	0.2432
cosine_precision@5	0.1565
cosine_precision@10	0.0846
cosine_recall@1	0.576
cosine_recall@3	0.7295
cosine_recall@5	0.7824
cosine_recall@10	0.8462
cosine_ndcg@10	0.7089
cosine_mrr@10	0.6653
cosine_map@100	0.6709
dot_accuracy@1	0.5263
dot_accuracy@3	0.6922
dot_accuracy@5	0.7494
dot_accuracy@10	0.8175
dot_precision@1	0.5263
dot_precision@3	0.2307
dot_precision@5	0.1499
dot_precision@10	0.0818
dot_recall@1	0.5263
dot_recall@3	0.6922
dot_recall@5	0.7494
dot_recall@10	0.8175
dot_ndcg@10	0.6697
dot_mrr@10	0.6226
dot_map@100	0.6291

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: 11.84 tokens
max: 31 tokens

min: 13 tokens
mean: 60.69 tokens
max: 149 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 11.84 tokens max: 31 tokens	min: 13 tokens mean: 60.69 tokens max: 149 tokens

Samples:

question	answer
`can dogs get pregnant when on their period?`	`2. Female dogs can only get pregnant when they're in heat. Some females will show physical signs of readiness – their discharge will lighten in color, and they will “flag,” or lift their tail up and to the side.`
`are there different forms of als?`	`['Sporadic ALS is the most common form. It affects up to 95% of people with the disease. Sporadic means it happens sometimes without a clear cause.', 'Familial ALS (FALS) runs in families. About 5% to 10% of people with ALS have this type. FALS is caused by changes to a gene.']`
`what is the difference between stayman and jacoby transfer?`	`1. The Stayman Convention is used only with a 4-Card Major suit looking for a 4-Card Major suit fit. Jacoby Transfer bids are used with a 5-Card suit looking for a 3-Card fit.`

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: 28 tokens

min: 19 tokens
mean: 61.37 tokens
max: 138 tokens

	question	answer
type	string	string
details	min: 8 tokens mean: 12.01 tokens max: 28 tokens	min: 19 tokens mean: 61.37 tokens max: 138 tokens

Samples:

question	answer
`is there a season 5 animal kingdom?`	`the good news for the fans is that the season five was confirmed by TNT in July, 2019. The season five of Animal Kingdom was expected to release in May, 2020.`
`what are cmos voltage levels?`	`CMOS gate circuits have input and output signal specifications that are quite different from TTL. For a CMOS gate operating at a power supply voltage of 5 volts, the acceptable input signal voltages range from 0 volts to 1.5 volts for a “low” logic state, and 3.5 volts to 5 volts for a “high” logic state.`
`dangers of drinking coke when pregnant?`	`Drinking it during pregnancy was linked to poorer fine motor, visual, spatial and visual motor abilities in early childhood (around age 3). By mid-childhood (age 7), kids whose moms drank diet sodas while pregnant had poorer verbal abilities, the study findings reported.`

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	-	-	0.2017
0.0000	1	2.584	-	-
0.0213	500	2.4164	-	-
0.0426	1000	1.1421	-	-
0.0639	1500	0.5215	-	-
0.0853	2000	0.3645	0.2763	0.6087
0.1066	2500	0.3046	-	-
0.1279	3000	0.2782	-	-
0.1492	3500	0.2601	-	-
0.1705	4000	0.2457	0.2013	0.6396
0.1918	4500	0.2363	-	-
0.2132	5000	0.2291	-	-
0.2345	5500	0.2217	-	-
0.2558	6000	0.2137	0.1770	0.6521
0.2771	6500	0.215	-	-
0.2984	7000	0.2057	-	-
0.3197	7500	0.198	-	-
0.3410	8000	0.196	0.1626	0.6594
0.3624	8500	0.1938	-	-
0.3837	9000	0.195	-	-
0.4050	9500	0.1895	-	-
0.4263	10000	0.186	0.1542	0.6628
0.4476	10500	0.1886	-	-
0.4689	11000	0.1835	-	-
0.4903	11500	0.1825	-	-
0.5116	12000	0.1804	0.1484	0.6638
0.5329	12500	0.176	-	-
0.5542	13000	0.1825	-	-
0.5755	13500	0.1785	-	-
0.5968	14000	0.1766	0.1436	0.6672
0.6182	14500	0.1718	-	-
0.6395	15000	0.1717	-	-
0.6608	15500	0.1674	-	-
0.6821	16000	0.1691	0.1406	0.6704
0.7034	16500	0.1705	-	-
0.7247	17000	0.1693	-	-
0.7460	17500	0.166	-	-
0.7674	18000	0.1676	0.1385	0.6721
0.7887	18500	0.1666	-	-
0.8100	19000	0.1658	-	-
0.8313	19500	0.1682	-	-
0.8526	20000	0.1639	0.1370	0.6705
0.8739	20500	0.1711	-	-
0.8953	21000	0.1667	-	-
0.9166	21500	0.165	-	-
0.9379	22000	0.1658	0.1356	0.6711
0.9592	22500	0.1665	-	-
0.9805	23000	0.1636	-	-
1.0	23457	-	-	0.6709

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 1.051 kWh
Carbon Emitted: 0.409 kg of CO2
Hours Used: 2.832 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}
}