metadata

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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:3012496
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: how to sign legal documents as power of attorney?
    sentences:
      - >-
        After the principal's name, write “by” and then sign your own name.
        Under or after the signature line, indicate your status as POA by
        including any of the following identifiers: as POA, as Agent, as
        Attorney in Fact or as Power of Attorney.
      - >-
        ['From the Home screen, swipe left to Apps.', 'Tap Transfer my Data.',
        'Tap Menu (...).', 'Tap Export to SD card.']
      - >-
        Ginger Dank Nugs (Grape) - 350mg. Feast your eyes on these unique and
        striking gourmet chocolates; Coco Nugs created by Ginger Dank. Crafted
        to resemble perfect nugs of cannabis, each of the 10 buds contains 35mg
        of THC. ... This is a perfect product for both cannabis and chocolate
        lovers, who appreciate a little twist.
  - source_sentence: how to delete vdom in fortigate?
    sentences:
      - >-
        Go to System -> VDOM -> VDOM2 and select 'Delete'. This VDOM is now
        successfully removed from the configuration.
      - >-
        Both combination birth control pills and progestin-only pills may cause
        headaches as a side effect. Additional side effects of birth control
        pills may include: breast tenderness. nausea.
      - >-
        White cheese tends to show imperfections more readily and as consumers
        got more used to yellow-orange cheese, it became an expected option.
        Today, many cheddars are yellow. While most cheesemakers use annatto,
        some use an artificial coloring agent instead, according to Sachs.
  - source_sentence: where are earthquakes most likely to occur on earth?
    sentences:
      - >-
        Zelle in the Bank of the America app is a fast, safe, and easy way to
        send and receive money with family and friends who have a bank account
        in the U.S., all with no fees. Money moves in minutes directly between
        accounts that are already enrolled with Zelle.
      - >-
        It takes about 3 days for a spacecraft to reach the Moon. During that
        time a spacecraft travels at least 240,000 miles (386,400 kilometers)
        which is the distance between Earth and the Moon.
      - >-
        Most earthquakes occur along the edge of the oceanic and continental
        plates. The earth's crust (the outer layer of the planet) is made up of
        several pieces, called plates. The plates under the oceans are called
        oceanic plates and the rest are continental plates.
  - source_sentence: fix iphone is disabled connect to itunes without itunes?
    sentences:
      - >-
        To fix a disabled iPhone or iPad without iTunes, you have to erase your
        device. Click on the "Erase iPhone" option and confirm your selection.
        Wait for a while as the "Find My iPhone" feature will remotely erase
        your iOS device. Needless to say, it will also disable its lock.
      - >-
        How Māui brought fire to the world. One evening, after eating a hearty
        meal, Māui lay beside his fire staring into the flames. ... In the
        middle of the night, while everyone was sleeping, Māui went from village
        to village and extinguished all the fires until not a single fire burned
        in the world.
      - >-
        Angry Orchard makes a variety of year-round craft cider styles,
        including Angry Orchard Crisp Apple, a fruit-forward hard cider that
        balances the sweetness of culinary apples with dryness and bright
        acidity of bittersweet apples for a complex, refreshing taste.
  - source_sentence: how to reverse a video on tiktok that's not yours?
    sentences:
      - >-
        ['Tap "Effects" at the bottom of your screen — it\'s an icon that looks
        like a clock. Open the Effects menu. ... ', 'At the end of the new list
        that appears, tap "Time." Select "Time" at the end. ... ', 'Select
        "Reverse" — you\'ll then see a preview of your new, reversed video
        appear on the screen.']
      - >-
        Franchise Facts Poke Bar has a franchise fee of up to $30,000, with a
        total initial investment range of $157,800 to $438,000. The initial cost
        of a franchise includes several fees -- Unlock this franchise to better
        understand the costs such as training and territory fees.
      - >-
        Relative age is the age of a rock layer (or the fossils it contains)
        compared to other layers. It can be determined by looking at the
        position of rock layers. Absolute age is the numeric age of a layer of
        rocks or fossils. Absolute age can be determined by using radiometric
        dating.
co2_eq_emissions:
  emissions: 6.448001991119035
  energy_consumed: 0.0165885485310573
  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: 0.109
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: gooaq 1024 dev
          type: gooaq-1024-dev
        metrics:
          - type: cosine_accuracy@1
            value: 0.6309
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8409
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8986
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9444
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6309
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28029999999999994
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17972000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09444000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6309
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8409
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8986
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9444
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7932643237589305
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7440336111111036
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7465739001132767
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: gooaq 512 dev
          type: gooaq-512-dev
        metrics:
          - type: cosine_accuracy@1
            value: 0.6271
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8366
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8946
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9431
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6271
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27886666666666665
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17892000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09431000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6271
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8366
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8946
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9431
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7904860196985286
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7408453174603101
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7434337897783787
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: gooaq 256 dev
          type: gooaq-256-dev
        metrics:
          - type: cosine_accuracy@1
            value: 0.6192
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.8235
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8866
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9364
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6192
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.27449999999999997
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17732000000000003
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09364000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6192
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8235
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8866
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9364
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7821476540310974
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7321259126984055
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7348893313013708
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: gooaq 128 dev
          type: gooaq-128-dev
        metrics:
          - type: cosine_accuracy@1
            value: 0.5942
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.804
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8721
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9249
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5942
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.268
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17442000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09249
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.5942
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.804
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8721
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9249
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7627845665665897
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.7103426587301529
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.7133975871277517
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: gooaq 64 dev
          type: gooaq-64-dev
        metrics:
          - type: cosine_accuracy@1
            value: 0.556
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7553
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8267
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8945
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.556
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.25176666666666664
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16534000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08945
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.556
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7553
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8267
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8945
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7246435400765202
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6701957142857087
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6743443703166442
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: gooaq 32 dev
          type: gooaq-32-dev
        metrics:
          - type: cosine_accuracy@1
            value: 0.4628
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.6619
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7415
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8241
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4628
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2206333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.1483
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08241
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.4628
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.6619
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.7415
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8241
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6387155548290799
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5797731349206319
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5857231820662888
            name: Cosine Map@100

Static Embeddings with BERT uncased tokenizer finetuned on GooAQ pairs

This is a sentence-transformers model trained on the gooaq dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

This model was trained using the train_script.py code.

Model Details

Model Description

Model Type: Sentence Transformer
Maximum Sequence Length: inf tokens
Output Dimensionality: 1024 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- 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): StaticEmbedding(
    (embedding): EmbeddingBag(30522, 1024, mode='mean')
  )
)

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/static-bert-uncased-gooaq")
# Run inference
sentences = [
    "how to reverse a video on tiktok that's not yours?",
    '[\'Tap "Effects" at the bottom of your screen — it\\\'s an icon that looks like a clock. Open the Effects menu. ... \', \'At the end of the new list that appears, tap "Time." Select "Time" at the end. ... \', \'Select "Reverse" — you\\\'ll then see a preview of your new, reversed video appear on the screen.\']',
    'Relative age is the age of a rock layer (or the fossils it contains) compared to other layers. It can be determined by looking at the position of rock layers. Absolute age is the numeric age of a layer of rocks or fossils. Absolute age can be determined by using radiometric dating.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

Evaluation

Metrics

Information Retrieval

Dataset: gooaq-1024-dev
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6309
cosine_accuracy@3	0.8409
cosine_accuracy@5	0.8986
cosine_accuracy@10	0.9444
cosine_precision@1	0.6309
cosine_precision@3	0.2803
cosine_precision@5	0.1797
cosine_precision@10	0.0944
cosine_recall@1	0.6309
cosine_recall@3	0.8409
cosine_recall@5	0.8986
cosine_recall@10	0.9444
cosine_ndcg@10	0.7933
cosine_mrr@10	0.744
cosine_map@100	0.7466

Information Retrieval

Dataset: gooaq-512-dev
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6271
cosine_accuracy@3	0.8366
cosine_accuracy@5	0.8946
cosine_accuracy@10	0.9431
cosine_precision@1	0.6271
cosine_precision@3	0.2789
cosine_precision@5	0.1789
cosine_precision@10	0.0943
cosine_recall@1	0.6271
cosine_recall@3	0.8366
cosine_recall@5	0.8946
cosine_recall@10	0.9431
cosine_ndcg@10	0.7905
cosine_mrr@10	0.7408
cosine_map@100	0.7434

Information Retrieval

Dataset: gooaq-256-dev
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6192
cosine_accuracy@3	0.8235
cosine_accuracy@5	0.8866
cosine_accuracy@10	0.9364
cosine_precision@1	0.6192
cosine_precision@3	0.2745
cosine_precision@5	0.1773
cosine_precision@10	0.0936
cosine_recall@1	0.6192
cosine_recall@3	0.8235
cosine_recall@5	0.8866
cosine_recall@10	0.9364
cosine_ndcg@10	0.7821
cosine_mrr@10	0.7321
cosine_map@100	0.7349

Information Retrieval

Dataset: gooaq-128-dev
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.5942
cosine_accuracy@3	0.804
cosine_accuracy@5	0.8721
cosine_accuracy@10	0.9249
cosine_precision@1	0.5942
cosine_precision@3	0.268
cosine_precision@5	0.1744
cosine_precision@10	0.0925
cosine_recall@1	0.5942
cosine_recall@3	0.804
cosine_recall@5	0.8721
cosine_recall@10	0.9249
cosine_ndcg@10	0.7628
cosine_mrr@10	0.7103
cosine_map@100	0.7134

Information Retrieval

Dataset: gooaq-64-dev
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.556
cosine_accuracy@3	0.7553
cosine_accuracy@5	0.8267
cosine_accuracy@10	0.8945
cosine_precision@1	0.556
cosine_precision@3	0.2518
cosine_precision@5	0.1653
cosine_precision@10	0.0895
cosine_recall@1	0.556
cosine_recall@3	0.7553
cosine_recall@5	0.8267
cosine_recall@10	0.8945
cosine_ndcg@10	0.7246
cosine_mrr@10	0.6702
cosine_map@100	0.6743

Information Retrieval

Dataset: gooaq-32-dev
Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.4628
cosine_accuracy@3	0.6619
cosine_accuracy@5	0.7415
cosine_accuracy@10	0.8241
cosine_precision@1	0.4628
cosine_precision@3	0.2206
cosine_precision@5	0.1483
cosine_precision@10	0.0824
cosine_recall@1	0.4628
cosine_recall@3	0.6619
cosine_recall@5	0.7415
cosine_recall@10	0.8241
cosine_ndcg@10	0.6387
cosine_mrr@10	0.5798
cosine_map@100	0.5857

Training Details

Training Dataset

gooaq

Dataset: gooaq at b089f72
Size: 3,012,496 training samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 18 characters
mean: 43.23 characters
max: 96 characters

min: 55 characters
mean: 253.36 characters
max: 371 characters

	question	answer
type	string	string
details	min: 18 characters mean: 43.23 characters max: 96 characters	min: 55 characters mean: 253.36 characters max: 371 characters

Samples:

question	answer
`what is the difference between broilers and layers?`	`An egg laying poultry is called egger or layer whereas broilers are reared for obtaining meat. So a layer should be able to produce more number of large sized eggs, without growing too much. On the other hand, a broiler should yield more meat and hence should be able to grow well.`
`what is the difference between chronological order and spatial order?`	`As a writer, you should always remember that unlike chronological order and the other organizational methods for data, spatial order does not take into account the time. Spatial order is primarily focused on the location. All it does is take into account the location of objects and not the time.`
`is kamagra same as viagra?`	`Kamagra is thought to contain the same active ingredient as Viagra, sildenafil citrate. In theory, it should work in much the same way as Viagra, taking about 45 minutes to take effect, and lasting for around 4-6 hours. However, this will vary from person to person.`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        512,
        256,
        128,
        64,
        32
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Evaluation Dataset

gooaq

Dataset: gooaq at b089f72
Size: 3,012,496 evaluation samples
Columns: question and answer
Approximate statistics based on the first 1000 samples:
question answer
type string string
details
min: 18 characters
mean: 43.17 characters
max: 98 characters

min: 51 characters
mean: 254.12 characters
max: 360 characters

	question	answer
type	string	string
details	min: 18 characters mean: 43.17 characters max: 98 characters	min: 51 characters mean: 254.12 characters max: 360 characters

Samples:

question	answer
`how do i program my directv remote with my tv?`	`['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']`
`are rodrigues fruit bats nocturnal?`	`Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.`
`why does your heart rate increase during exercise bbc bitesize?`	`During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.`

Loss: MatryoshkaLoss with these parameters:

{
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        1024,
        512,
        256,
        128,
        64,
        32
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 2048
per_device_eval_batch_size: 2048
learning_rate: 0.2
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: 2048
per_device_eval_batch_size: 2048
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 0.2
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
eval_on_start: False
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	gooaq-1024-dev_cosine_map@100	gooaq-512-dev_cosine_map@100	gooaq-256-dev_cosine_map@100	gooaq-128-dev_cosine_map@100	gooaq-64-dev_cosine_map@100	gooaq-32-dev_cosine_map@100
0	0	-	-	0.2095	0.2010	0.1735	0.1381	0.0750	0.0331
0.0007	1	34.953	-	-	-	-	-	-	-
0.0682	100	16.2504	-	-	-	-	-	-	-
0.1363	200	5.9502	-	-	-	-	-	-	-
0.1704	250	-	1.6781	0.6791	0.6729	0.6619	0.6409	0.5904	0.4934
0.2045	300	4.8411	-	-	-	-	-	-	-
0.2727	400	4.336	-	-	-	-	-	-	-
0.3408	500	4.0484	1.3935	0.7104	0.7055	0.6968	0.6756	0.6322	0.5358
0.4090	600	3.8378	-	-	-	-	-	-	-
0.4772	700	3.6765	-	-	-	-	-	-	-
0.5112	750	-	1.2549	0.7246	0.7216	0.7133	0.6943	0.6482	0.5582
0.5453	800	3.5439	-	-	-	-	-	-	-
0.6135	900	3.4284	-	-	-	-	-	-	-
0.6817	1000	3.3576	1.1656	0.7359	0.7338	0.7252	0.7040	0.6604	0.5715
0.7498	1100	3.2456	-	-	-	-	-	-	-
0.8180	1200	3.2014	-	-	-	-	-	-	-
0.8521	1250	-	1.1133	0.7438	0.7398	0.7310	0.7099	0.6704	0.5796
0.8862	1300	3.1536	-	-	-	-	-	-	-
0.9543	1400	3.0696	-	-	-	-	-	-	-
1.0	1467	-	-	0.7466	0.7434	0.7349	0.7134	0.6743	0.5857

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.017 kWh
Carbon Emitted: 0.006 kg of CO2
Hours Used: 0.109 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: 3.2.0.dev0
Transformers: 4.43.4
PyTorch: 2.5.0.dev20240807+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",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

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}
}