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, 'architecture': '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("ar9av/tsdae-civic-bert")
# Run inference
sentences = [
    "right o'clock Oh Jack here . Man brought team 3rd November . going'll to . We Pledge of and . And we got guests Manager Brantley'll of . the . I appeal flag the United States of and to Republic which,, for . Let . God, come before this grateful live wonderful community We serve our community members in our respective capacities morning and give us eyes and full hearts, Lord, as handle business Thank you your, In Christ name amen Thank Price the pledge . First item is",
    "All right, it's nine o'clock. Oh, Jack's here too. Man, y'all brought the whole team. 9 o'clock, November 3rd, November already. Gosh. Time keeps on going. We'll call the meeting to order. We'll open with the Pledge of Allegiance and prayer. And since we've got special guests, I'll ask Town Manager Brantley Price if he'll lead us in the Pledge of Allegiance. And I'll say the prayer. I appeal to the flag of the United States of America and to the Republic for which it stands, one nation under God, indivisible, with liberty and justice for all. Let's pray together. God, we come before you this morning with grateful hearts to live in such a wonderful community. We're grateful for the opportunity to serve our community members in our respective capacities here this morning. Be with us and give us clear eyes and full hearts, Lord, as we handle the people's business. Thank you for your many, many blessings. In Christ's name, amen. Amen. Thank you, Manager Price, for the pledge. First item is",
    "for Wednesday, November 12th\nat 7:05 p.m. What you want? This meeting is being recorded. report anything. I have\nYeah. agenda\nbut there were other items\n>> that didn't get to this print Right. >> Caught you off guard there. >> I know you did. I'm not. Oh, so it\nworked very well. >> what did what did we end up\nbecause of the day? >> that's all right. You're fine. >> All right. So, we're still going to go\na wedding for\nI think we're gonna\nwant to go. All right. No problem. 1.7. First one is Parker Hill\nWell, it's already been\nConservation Commission. Yeah. And since it's not supposed to be\nwhat snacks,\n>> By the way, I I did autographs. put your\nyour name on your agenda and stuff and\nYou mind just announcing\nthat you've arrived and what time? Did provide? Guess we should wait. kind of that was\n>> right. So what doesn't go into the\ngeneral account instead\nof meeting\nI thought we had a\nCan you read it off? Oh, you\nYou just repeat the number.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.8891, 0.8695],
#         [0.8891, 1.0000, 0.8283],
#         [0.8695, 0.8283, 1.0000]])

Training Details

Training Dataset

Unnamed Dataset

Size: 10,000 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 3 tokens
mean: 91.75 tokens
max: 202 tokens
min: 13 tokens
mean: 223.56 tokens
max: 256 tokens

Samples:

sentence_0	sentence_1
`right I call this town council to order on Recognize is a quorum all those who attendance in those are in online . hello Davis are tonight . Thanks, I We having you . and expeditious tonight a big game JMU at . like have just a wo we've had two works who their a of for Our are with them time Ms. we have?? .? Here?? Here . Mr. Hunter? Here Hardy`	All right, I'd like to call this town council meeting to order on this November 12, 2025. Recognize there is a quorum present. Welcome all those who are in attendance in person and those who are tuned in online. A special hello to Dr. Davis's local and state government class students who are here tonight. Thanks for coming, although I think you were probably told you had to. We appreciate having you. We'll try and be expeditious in the people's business tonight because I know there's a big game later this evening between Longwood and JMU here at home. I'd also like to have just a moment. I won't mention names, but we've had two of our public works employees who lost their wives here very recently. And so we'll just have a brief moment of silence for them. Thank you. Our prayers and our hearts are with them at this difficult time. Ms. McKay, can we have a roll call, please? Mrs. Amos? Here. Mr. Reed? Here. Mr. Dwyer? Here. Mr. Parrott? Here. Mr. Yoland. Here. Mr. Hunter? Here. Mr. Hardy
`everyone November the City Columbia Board of Chair for meeting like introduce the members: Harding'm, Davis Whittle, Sidney Bang, Duvall also to introduce the staff the, Andrew, Board, Erica Hyan, Deputy and Madeline Land . is special, appeals . for the record, wishing and come the . No can floor When come the podium state your and speak the because meeting is recorded Applicants the board`	Welcome, everyone, to the November meeting of the City of Columbia Board of Zoning Appeals. I am Catherine Fenner, Chair for the Board, and will be serving as the chair for today's meeting. I would like to introduce the other members of the board: Josh Harding, I'm sorry, Davis Whittle, Sidney Lanham, Jonathan Bang, and Sherard Duvall. I would also like to introduce the staff that assists the board, Andrew Livingood, Zoning Board Administrator, Erica Hyan, Deputy Zoning Administrator, and Madeline Bowden, Land Use Board Coordinator. The board is charged with hearing applications for special exceptions, variances, and administrative appeals. All testimony is recorded for the record, and anyone wishing to speak will need to be sworn in and come to the podium to speak. No testimony can be taken from the floor. When you come to the podium, state your name and please speak clearly into the microphone because this meeting is being recorded. Applicants with cases before the board are allotted
`Corporation 7:30 WEDC Room 250 Highway Texas TO & PLEDGE OF ON ITEMS member the may Board not Agenda of the fill out a form meeting . that comments limited minutes for, six In addition, is not allowed to converse deliberate take on any presented CONSENT AGENDA matters Agenda are be routine by Board will be will not of items . discussion desired, that item will removed from the separately . act upon`	Wylie Economic Development Corporation [object Object]Board Regular Meeting [object Object]November 19, 2025 – 7:30 AM [object Object]WEDC Office Conference Room - 250 South Highway 78, Wylie, Texas [object Object]75098 [object Object] [object Object] [object Object] [object Object] [object Object] [object Object]CALL TO ORDER [object Object]INVOCATION & PLEDGE OF ALLEGIANCE [object Object]COMMENTS ON NON-AGENDA ITEMS [object Object]Any member of the public may address Board regarding an item that is not listed on the Agenda. Members of the public must [object Object]fill out a form prior to the meeting in order to speak. Board requests that comments be limited to three minutes for an individual, [object Object]six minutes for a group. In addition, Board is not allowed to converse, deliberate or take action on any matter presented during [object Object]citizen participation. CONSENT AGENDA [object Object]All matters listed under the Consent Agenda are considered to be routine by the Board and will be enacted by one motion. There will not be separate discussion of these items. If discussion is desired, that item will be removed from the Consent Agenda [object Object]and will be considered separately. A. Consider and act upon appr

Loss: DenoisingAutoEncoderLoss

Training Hyperparameters

Non-Default Hyperparameters

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: 8
per_device_eval_batch_size: 8
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
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
project: huggingface
trackio_space_id: trackio
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
hub_revision: None
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: no
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: True
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss
0.4	500	4.3532
0.8	1000	3.4547

Framework Versions

Python: 3.11.10
Sentence Transformers: 5.2.0
Transformers: 4.57.3
PyTorch: 2.4.1+cu124
Accelerate: 1.12.0
Datasets: 4.4.2
Tokenizers: 0.22.1

Citation

BibTeX

Sentence Transformers

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",
}

DenoisingAutoEncoderLoss

bibtex

@inproceedings{wang-2021-TSDAE,
    title = "TSDAE: Using Transformer-based Sequential Denoising Auto-Encoderfor Unsupervised Sentence Embedding Learning",
    author = "Wang, Kexin and Reimers, Nils and Gurevych, Iryna",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    pages = "671--688",
    url = "https://arxiv.org/abs/2104.06979",
}

	sentence_0	sentence_1
type	string	string
details	min: 3 tokens mean: 91.75 tokens max: 202 tokens	min: 13 tokens mean: 223.56 tokens max: 256 tokens