This is a sentence-transformers model finetuned from google-bert/bert-base-uncased. 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: 256 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity

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': 256, '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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("gabrielegabellone/bert-base-uncased-snli")
# Run inference
sentences = [
    'A dog running through tall grass.',
    'Dog lying in an open field.',
    'The woman is outside.',
]
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

Unnamed Dataset

Size: 549,367 training samples
Columns: sentence_0, sentence_1, and label
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1 label
type string string int
details
min: 6 tokens
mean: 16.75 tokens
max: 73 tokens
min: 4 tokens
mean: 10.57 tokens
max: 29 tokens
0: ~36.60%
1: ~33.00%
2: ~30.40%

Samples:

sentence_0	sentence_1	label
`A woman in wearing a white hat holding a scythe and a cutting of wheat.`	`A sad woman in wearing a white hat holding a scythe and a cutting of wheat.`	`1`
`There is an old man sitting on the edge of a concrete pillar wearing a hat.`	`A man sits outside.`	`0`
`A man dressed as a cook enjoys a meal and bottle of wine in an empty restaurant.`	`There is a chef that is waiting for people to come to his restaurant and is preparing food.`	`1`

Loss: SoftmaxLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 1
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
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
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: 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: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
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
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
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.0146	500	0.9507
0.0291	1000	0.8171
0.0437	1500	0.7619
0.0582	2000	0.7505
0.0728	2500	0.7221
0.0874	3000	0.6913
0.1019	3500	0.6753
0.1165	4000	0.6658
0.1311	4500	0.6578
0.1456	5000	0.6438
0.1602	5500	0.6348
0.1747	6000	0.6286
0.1893	6500	0.6261
0.2039	7000	0.6154
0.2184	7500	0.6184
0.2330	8000	0.6107
0.2476	8500	0.6189
0.2621	9000	0.6087
0.2767	9500	0.612
0.2912	10000	0.6026
0.3058	10500	0.603
0.3204	11000	0.5987
0.3349	11500	0.5999
0.3495	12000	0.5893
0.3640	12500	0.5767
0.3786	13000	0.59
0.3932	13500	0.5898
0.4077	14000	0.5854
0.4223	14500	0.571
0.4369	15000	0.5811
0.4514	15500	0.569
0.4660	16000	0.5751
0.4805	16500	0.5714
0.4951	17000	0.5605
0.5097	17500	0.5687
0.5242	18000	0.5566
0.5388	18500	0.5642
0.5534	19000	0.5468
0.5679	19500	0.5733
0.5825	20000	0.5582
0.5970	20500	0.5534
0.6116	21000	0.5417
0.6262	21500	0.5483
0.6407	22000	0.5446
0.6553	22500	0.5581
0.6699	23000	0.5353
0.6844	23500	0.5513
0.6990	24000	0.5521
0.7135	24500	0.5402
0.7281	25000	0.5371
0.7427	25500	0.554
0.7572	26000	0.5322
0.7718	26500	0.5404
0.7863	27000	0.5372
0.8009	27500	0.5465
0.8155	28000	0.5387
0.8300	28500	0.5363
0.8446	29000	0.5296
0.8592	29500	0.5356
0.8737	30000	0.5341
0.8883	30500	0.54
0.9028	31000	0.527
0.9174	31500	0.5261
0.9320	32000	0.5424
0.9465	32500	0.5286
0.9611	33000	0.5262
0.9757	33500	0.5255
0.9902	34000	0.5221

Framework Versions

Python: 3.11.12
Sentence Transformers: 4.1.0
Transformers: 4.52.3
PyTorch: 2.6.0+cu124
Accelerate: 1.7.0
Datasets: 3.6.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers and SoftmaxLoss

bibtex

@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",
}

	sentence_0	sentence_1	label
type	string	string	int
details	min: 6 tokens mean: 16.75 tokens max: 73 tokens	min: 4 tokens mean: 10.57 tokens max: 29 tokens	0: ~36.60% 1: ~33.00% 2: ~30.40%