metadata

base_model: google-bert/bert-base-uncased
datasets:
  - sentence-transformers/stsb
language:
  - en
library_name: sentence-transformers
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:20127
  - loss:CosineSimilarityLoss
widget:
  - source_sentence: The man talked to a girl over the internet camera.
    sentences:
      - A group of elderly people pose around a dining table.
      - A teenager talks to a girl over a webcam.
      - There is no 'still' that is not relative to some other object.
  - source_sentence: A woman is writing something.
    sentences:
      - Two eagles are perched on a branch.
      - >-
        It refers to the maximum f-stop (which is defined as the ratio of focal
        length to effective aperture diameter).
      - A woman is chopping green onions.
  - source_sentence: The player shoots the winning points.
    sentences:
      - Minimum wage laws hurt the least skilled, least productive the most.
      - The basketball player is about to score points for his team.
      - Sheep are grazing in the field in front of a line of trees.
  - source_sentence: >-
      Stars form in star-formation regions, which itself develop from molecular
      clouds.
    sentences:
      - >-
        Although I believe Searle is mistaken, I don't think you have found the
        problem.
      - >-
        It may be possible for a solar system like ours to exist outside of a
        galaxy.
      - >-
        A blond-haired child performing on the trumpet in front of a house while
        his younger brother watches.
  - source_sentence: >-
      While Queen may refer to both Queen regent (sovereign) or Queen consort,
      the King has always been the sovereign.
    sentences:
      - At first, I thought this is a bit of a tricky question.
      - A man sitting on the floor in a room is strumming a guitar.
      - >-
        There is a very good reason not to refer to the Queen's spouse as "King"
        - because they aren't the King.
model-index:
  - name: SentenceTransformer based on google-bert/bert-base-uncased
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.8704036241540303
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8723063947160014
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8240304398880643
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8326280427400794
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.824332157368767
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8327621115149644
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.7561120117358238
            name: Pearson Dot
          - type: spearman_dot
            value: 0.7732899193523305
            name: Spearman Dot
          - type: pearson_max
            value: 0.8704036241540303
            name: Pearson Max
          - type: spearman_max
            value: 0.8723063947160014
            name: Spearman Max
          - type: pearson_cosine
            value: 0.8341388917194029
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.8312253997736475
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.8121299512156789
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.8102823785744042
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.8124379587910084
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.8106160221464417
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.6947485972044003
            name: Pearson Dot
          - type: spearman_dot
            value: 0.6858002756760537
            name: Spearman Dot
          - type: pearson_max
            value: 0.8341388917194029
            name: Pearson Max
          - type: spearman_max
            value: 0.8312253997736475
            name: Spearman Max

SentenceTransformer based on google-bert/bert-base-uncased

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased on the sentence-transformers/stsb 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: 128 tokens
Output Dimensionality: 768 tokens
Similarity Function: Cosine Similarity
Training Dataset:
- sentence-transformers/stsb
Language: en

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': 128, '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("armaniii/bert-base-uncased-augmentation-indomain-bm25-sts")
# Run inference
sentences = [
    'While Queen may refer to both Queen regent (sovereign) or Queen consort, the King has always been the sovereign.',
    'There is a very good reason not to refer to the Queen\'s spouse as "King" - because they aren\'t the King.',
    'A man sitting on the floor in a room is strumming a guitar.',
]
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

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8704
spearman_cosine	0.8723
pearson_manhattan	0.824
spearman_manhattan	0.8326
pearson_euclidean	0.8243
spearman_euclidean	0.8328
pearson_dot	0.7561
spearman_dot	0.7733
pearson_max	0.8704
spearman_max	0.8723

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.8341
spearman_cosine	0.8312
pearson_manhattan	0.8121
spearman_manhattan	0.8103
pearson_euclidean	0.8124
spearman_euclidean	0.8106
pearson_dot	0.6947
spearman_dot	0.6858
pearson_max	0.8341
spearman_max	0.8312

Training Details

Training Dataset

sentence-transformers/stsb

Dataset: sentence-transformers/stsb
Size: 20,127 training samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 6 tokens
mean: 10.0 tokens
max: 28 tokens

min: 5 tokens
mean: 9.95 tokens
max: 25 tokens

min: 0.0
mean: 0.47
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 6 tokens mean: 10.0 tokens max: 28 tokens	min: 5 tokens mean: 9.95 tokens max: 25 tokens	min: 0.0 mean: 0.47 max: 1.0

Samples:

sentence1	sentence2	score
`A plane is taking off.`	`An air plane is taking off.`	`1.0`
`A man is playing a large flute.`	`A man is playing a flute.`	`0.76`
`A man is spreading shreded cheese on a pizza.`	`A man is spreading shredded cheese on an uncooked pizza.`	`0.76`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Evaluation Dataset

sentence-transformers/stsb

Dataset: sentence-transformers/stsb
Size: 1,500 evaluation samples
Columns: sentence1, sentence2, and score
Approximate statistics based on the first 1000 samples:
sentence1 sentence2 score
type string string float
details
min: 5 tokens
mean: 15.1 tokens
max: 45 tokens

min: 6 tokens
mean: 15.11 tokens
max: 53 tokens

min: 0.0
mean: 0.47
max: 1.0

	sentence1	sentence2	score
type	string	string	float
details	min: 5 tokens mean: 15.1 tokens max: 45 tokens	min: 6 tokens mean: 15.11 tokens max: 53 tokens	min: 0.0 mean: 0.47 max: 1.0

Samples:

sentence1	sentence2	score
`A man with a hard hat is dancing.`	`A man wearing a hard hat is dancing.`	`1.0`
`A young child is riding a horse.`	`A child is riding a horse.`	`0.95`
`A man is feeding a mouse to a snake.`	`The man is feeding a mouse to the snake.`	`1.0`

Loss: CosineSimilarityLoss with these parameters:

{
    "loss_fct": "torch.nn.modules.loss.MSELoss"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
warmup_ratio: 0.1
fp16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
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: 5e-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: False
fp16: True
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: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-test_spearman_cosine
0.0795	100	0.0526	0.0390	0.8215
0.1590	200	0.0218	0.0335	0.8415
0.2385	300	0.0186	0.0310	0.8561
0.3180	400	0.0166	0.0341	0.8479
0.3975	500	0.0176	0.0313	0.8503
0.4769	600	0.0155	0.0281	0.8652
0.5564	700	0.0148	0.0270	0.8656
0.6359	800	0.014	0.0277	0.8669
0.7154	900	0.0149	0.0286	0.8694
0.7949	1000	0.0125	0.0281	0.8724
0.8744	1100	0.013	0.0285	0.8694
0.9539	1200	0.0127	0.0269	0.8723
1.0	1258	-	-	0.8312

Framework Versions

Python: 3.9.2
Sentence Transformers: 3.0.1
Transformers: 4.43.1
PyTorch: 2.3.1+cu121
Accelerate: 0.34.2
Datasets: 2.14.7
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",
}