metadata

base_model: google-bert/bert-base-uncased
datasets:
  - stanfordnlp/snli
language:
  - en
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:494430
  - loss:SoftmaxLoss
widget:
  - source_sentence: A person out front of a business with a woman statue holding a bottle.
    sentences:
      - A couple holds hands.
      - The young boy is upside down.
      - the man is baking some bread
  - source_sentence: A person is dressed up in a weird costume with a red tongue sticking out.
    sentences:
      - thhe man plays a tuba
      - Four siblings are climbing on a fake black bear.
      - the tongue is blue
  - source_sentence: A man on a train is talking on a cellphone.
    sentences:
      - A man is playing a flute on a bus.
      - The woman is sexy.
      - two cyclists racing
  - source_sentence: An elderly woman giving her daughter a hug.
    sentences:
      - There are two women hugging.
      - A man holds a flag on the street.
      - people are sitting on a red roofed bus going to a museum
  - source_sentence: >-
      A pilot dressed in a dark-colored sweater is sitting in the cock-pit of a
      plane with his hands crossed.
    sentences:
      - A pilot is sitting in his plain with his hands crossed
      - The boys are playing outside on a log.
      - Two men discuss their love lives.

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

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased on the stanfordnlp/snli 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:
- stanfordnlp/snli
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("hcy5561/distilroberta-base-sentence-transformer-snli")
# Run inference
sentences = [
    'A pilot dressed in a dark-colored sweater is sitting in the cock-pit of a plane with his hands crossed.',
    'A pilot is sitting in his plain with his hands crossed',
    'The boys are playing outside on a log.',
]
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]

Training Details

Training Dataset

stanfordnlp/snli

Dataset: stanfordnlp/snli at cdb5c3d
Size: 494,430 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: 16.24 tokens
max: 50 tokens

min: 4 tokens
mean: 10.55 tokens
max: 26 tokens

0: ~31.10%
1: ~33.40%
2: ~35.50%

	premise	hypothesis	label
type	string	string	int
details	min: 6 tokens mean: 16.24 tokens max: 50 tokens	min: 4 tokens mean: 10.55 tokens max: 26 tokens	0: ~31.10% 1: ~33.40% 2: ~35.50%

Samples:

premise	hypothesis	label
`Two men, one in yellow, are on a wooden boat.`	`Two men swimming in water`	`2`
`Two people sleep on a couch.`	`Two people are asleep.`	`0`
`a little boy is learning to swim with the help of a float board.`	`The boy is crawling.`	`2`

Loss: SoftmaxLoss

Evaluation Dataset

stanfordnlp/snli

Dataset: stanfordnlp/snli at cdb5c3d
Size: 27,468 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: 16.66 tokens
max: 44 tokens

min: 4 tokens
mean: 10.48 tokens
max: 31 tokens

0: ~36.10%
1: ~31.80%
2: ~32.10%

	premise	hypothesis	label
type	string	string	int
details	min: 6 tokens mean: 16.66 tokens max: 44 tokens	min: 4 tokens mean: 10.48 tokens max: 31 tokens	0: ~36.10% 1: ~31.80% 2: ~32.10%

Samples:

premise	hypothesis	label
`A taxi cab driver looks stressed out in his car.`	`a taxi driver is stressed`	`0`
`Two men do trick in a park.`	`The men only sat on the bench in the park, doing nothing.`	`2`
`Two woman walking, the blond is looking at the camera wearing sunglasses making an oh face.`	`One lady makes a shocked face at the camera as the photographer tells the women they are lost.`	`1`

Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 64
per_device_eval_batch_size: 64
num_train_epochs: 4
warmup_ratio: 0.1
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
prediction_loss_only: True
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
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: 4
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: False
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}
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
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: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss
0.1294	1000	0.9208	0.7448
0.2589	2000	0.7095	0.6462
0.3883	3000	0.6415	0.6199
0.5177	4000	0.6125	0.5940
0.6472	5000	0.5935	0.5672
0.7766	6000	0.5748	0.5550
0.9060	7000	0.5654	0.5506
1.0355	8000	0.5524	0.5376
1.1649	9000	0.5386	0.5319
1.2943	10000	0.5192	0.5361
1.4238	11000	0.4863	0.5304
1.5532	12000	0.4687	0.5278
1.6826	13000	0.4586	0.5305
1.8121	14000	0.4474	0.5222
1.9415	15000	0.4447	0.5237
2.0709	16000	0.434	0.5172
2.2004	17000	0.4243	0.5235
2.3298	18000	0.398	0.5224
2.4592	19000	0.3747	0.5344
2.5887	20000	0.3669	0.5301
2.7181	21000	0.3583	0.5406
2.8475	22000	0.3496	0.5354
2.9770	23000	0.3527	0.5324
3.1064	24000	0.3419	0.5299
3.2358	25000	0.3358	0.5456
3.3653	26000	0.3096	0.5562
3.4947	27000	0.2964	0.5644
3.6241	28000	0.2998	0.5565
3.7536	29000	0.2906	0.5590
3.8830	30000	0.2923	0.5564

Framework Versions

Python: 3.10.6
Sentence Transformers: 3.0.1
Transformers: 4.39.3
PyTorch: 2.2.2+cu118
Accelerate: 0.28.0
Datasets: 2.20.0
Tokenizers: 0.15.2

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",
}