metadata

language:
  - en
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - loss:SoftmaxLoss
base_model: google-bert/bert-base-uncased
metrics:
  - pearson_cosine
  - spearman_cosine
  - pearson_manhattan
  - spearman_manhattan
  - pearson_euclidean
  - spearman_euclidean
  - pearson_dot
  - spearman_dot
  - pearson_max
  - spearman_max
widget:
  - source_sentence: the guy is dead
    sentences:
      - The dog is dead.
      - The man is training the dog.
      - People gather for an event.
  - source_sentence: the boy is five
    sentences:
      - The girl is five years old.
      - A man sits in a hotel lobby.
      - The man is laying on the couch.
  - source_sentence: a guy is waxing
    sentences:
      - A woman is making music.
      - A girl is laying in the pool
      - She is the boy's aunt.
  - source_sentence: Dog herding cows
    sentences:
      - A woman is walking her dog.
      - Both people are standing up.
      - The women are friends.
  - source_sentence: There is a party
    sentences:
      - people take pictures
      - A man is repainting a garage
      - the crew all ate lunch alone
pipeline_tag: sentence-similarity
co2_eq_emissions:
  emissions: 3.4540412355858656
  energy_consumed: 0.008886090721390334
  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: 0.049
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
  - name: SentenceTransformer based on google-bert/bert-base-uncased
    results:
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts dev
          type: sts-dev
        metrics:
          - type: pearson_cosine
            value: 0.5998264726332272
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.6439392261876368
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.6232915971361167
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.6407370027700541
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.6204725584722414
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.6394239914170929
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.4799617911944018
            name: Pearson Dot
          - type: spearman_dot
            value: 0.4939854901099171
            name: Spearman Dot
          - type: pearson_max
            value: 0.6232915971361167
            name: Pearson Max
          - type: spearman_max
            value: 0.6439392261876368
            name: Spearman Max
      - task:
          type: semantic-similarity
          name: Semantic Similarity
        dataset:
          name: sts test
          type: sts-test
        metrics:
          - type: pearson_cosine
            value: 0.5516604742812986
            name: Pearson Cosine
          - type: spearman_cosine
            value: 0.5840596347673308
            name: Spearman Cosine
          - type: pearson_manhattan
            value: 0.5842488902993314
            name: Pearson Manhattan
          - type: spearman_manhattan
            value: 0.5886614741524346
            name: Spearman Manhattan
          - type: pearson_euclidean
            value: 0.582443715857982
            name: Pearson Euclidean
          - type: spearman_euclidean
            value: 0.5869827075201962
            name: Spearman Euclidean
          - type: pearson_dot
            value: 0.4054565422297012
            name: Pearson Dot
          - type: spearman_dot
            value: 0.40476618101346834
            name: Spearman Dot
          - type: pearson_max
            value: 0.5842488902993314
            name: Pearson Max
          - type: spearman_max
            value: 0.5886614741524346
            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/all-nli 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/all-nli
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': 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-nli-v1")
# Run inference
sentences = [
    'There is a party',
    'people take pictures',
    'A man is repainting a garage',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Semantic Similarity

Dataset: sts-dev
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.5998
spearman_cosine	0.6439
pearson_manhattan	0.6233
spearman_manhattan	0.6407
pearson_euclidean	0.6205
spearman_euclidean	0.6394
pearson_dot	0.48
spearman_dot	0.494
pearson_max	0.6233
spearman_max	0.6439

Semantic Similarity

Dataset: sts-test
Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.5517
spearman_cosine	0.5841
pearson_manhattan	0.5842
spearman_manhattan	0.5887
pearson_euclidean	0.5824
spearman_euclidean	0.587
pearson_dot	0.4055
spearman_dot	0.4048
pearson_max	0.5842
spearman_max	0.5887

Training Details

Training Dataset

sentence-transformers/all-nli

Dataset: sentence-transformers/all-nli at cc6c526
Size: 10,000 training samples
Columns: premise, hypothesis, and label
Approximate statistics based on the first 1000 samples:
premise hypothesis label
type string string int
details
min: 6 tokens
mean: 17.38 tokens
max: 52 tokens

min: 4 tokens
mean: 10.7 tokens
max: 31 tokens

0: ~33.40%
1: ~33.30%
2: ~33.30%

	premise	hypothesis	label
type	string	string	int
details	min: 6 tokens mean: 17.38 tokens max: 52 tokens	min: 4 tokens mean: 10.7 tokens max: 31 tokens	0: ~33.40% 1: ~33.30% 2: ~33.30%

Samples:

premise	hypothesis	label
`A person on a horse jumps over a broken down airplane.`	`A person is training his horse for a competition.`	`1`
`A person on a horse jumps over a broken down airplane.`	`A person is at a diner, ordering an omelette.`	`2`
`A person on a horse jumps over a broken down airplane.`	`A person is outdoors, on a horse.`	`0`

Loss: SoftmaxLoss

Evaluation Dataset

sentence-transformers/all-nli

Dataset: sentence-transformers/all-nli at cc6c526
Size: 1,000 evaluation samples
Columns: premise, hypothesis, and label
Approximate statistics based on the first 1000 samples:
premise hypothesis label
type string string int
details
min: 6 tokens
mean: 18.44 tokens
max: 57 tokens

min: 5 tokens
mean: 10.57 tokens
max: 25 tokens

0: ~33.10%
1: ~33.30%
2: ~33.60%

	premise	hypothesis	label
type	string	string	int
details	min: 6 tokens mean: 18.44 tokens max: 57 tokens	min: 5 tokens mean: 10.57 tokens max: 25 tokens	0: ~33.10% 1: ~33.30% 2: ~33.60%

Samples:

premise	hypothesis	label
`Two women are embracing while holding to go packages.`	`The sisters are hugging goodbye while holding to go packages after just eating lunch.`	`1`
`Two women are embracing while holding to go packages.`	`Two woman are holding packages.`	`0`
`Two women are embracing while holding to go packages.`	`The men are fighting outside a deli.`	`2`

Loss: SoftmaxLoss

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
bf16: True

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: False
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
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
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: None
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_sampler: batch_sampler
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-dev_spearman_cosine	sts-test_spearman_cosine
0	0	-	-	0.5931	-
0.16	100	1.056	0.9278	0.6555	-
0.32	200	0.8966	0.8751	0.6381	-
0.48	300	0.8646	0.8393	0.6170	-
0.64	400	0.8328	0.8100	0.5804	-
0.8	500	0.8307	0.7940	0.6413	-
0.96	600	0.8373	0.7602	0.6439	-
1.0	625	-	-	-	0.5841

Environmental Impact

Carbon emissions were measured using CodeCarbon.

Energy Consumed: 0.009 kWh
Carbon Emitted: 0.003 kg of CO2
Hours Used: 0.049 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: 3.0.0.dev0
Transformers: 4.41.0.dev0
PyTorch: 2.3.0+cu121
Accelerate: 0.26.1
Datasets: 2.18.0
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

@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",
}