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: 512 tokens
Output Dimensionality: 768 tokens
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': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("T-Blue/tsdae_pro_mbert")
# Run inference
sentences = [
    '𑀫च𑀢𑀲𑀢 𑀳न𑀪𑁦𑀟𑀦 च',
    ' 𑀳𑀫त𑀫𑁦𑀪ढचप𑀢न𑀞 पच 𑀫च𑀢𑀲𑀢 ञच𑀦 𑀳न𑀪𑁦𑀟𑀦 च त𑀢𑀞𑀢𑀟 𑀭थ𑀖𑀗𑀯',
    '𑀯',
]
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: 97,043 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: 5.12 tokens
max: 30 tokens
min: 3 tokens
mean: 9.06 tokens
max: 56 tokens
Samples:
sentence_0 sentence_1
च𑀞𑀱च𑀢 च𑀞𑀱च𑀢 𑀭ठ𑀯
ठ𑀧𑀧𑁢𑀯 ठ𑀧𑀧𑁢𑀯
𑁢𑀗𑀯 𑁢𑀗𑀯
Loss: DenoisingAutoEncoderLoss

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
per_device_eval_batch_size: 16
num_train_epochs: 5
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
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: 5
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: 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
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.0824	500	1.1372
0.1649	1000	0.8075
0.2473	1500	0.7708
0.3297	2000	0.7464
0.4121	2500	0.7286
0.4946	3000	0.7187
0.5770	3500	0.7089
0.6594	4000	0.6942
0.7418	4500	0.7022
0.8243	5000	0.6939
0.9067	5500	0.6859
0.9891	6000	0.6807
1.0715	6500	0.6841
1.1540	7000	0.6764
1.2364	7500	0.6705
1.3188	8000	0.6712
1.4013	8500	0.6683
1.4837	9000	0.6662
1.5661	9500	0.6635
1.6485	10000	0.655
1.7310	10500	0.6667
1.8134	11000	0.6533
1.8958	11500	0.6564
1.9782	12000	0.646
2.0607	12500	0.6522
2.1431	13000	0.6466
2.2255	13500	0.6464
2.3079	14000	0.647
2.3904	14500	0.6408
2.4728	15000	0.6415
2.5552	15500	0.6397
2.6377	16000	0.6303
2.7201	16500	0.6465
2.8025	17000	0.6287
2.8849	17500	0.6358
2.9674	18000	0.6247
3.0498	18500	0.6318
3.1322	19000	0.627
3.2146	19500	0.6222
3.2971	20000	0.6262
3.3795	20500	0.6197
3.4619	21000	0.6234
3.5443	21500	0.6193
3.6268	22000	0.6088
3.7092	22500	0.624
3.7916	23000	0.6089
3.8741	23500	0.6184
3.9565	24000	0.6047
4.0389	24500	0.6066
4.1213	25000	0.6082
4.2038	25500	0.5999
4.2862	26000	0.6046
4.3686	26500	0.6038
4.4510	27000	0.5978
4.5335	27500	0.5948
4.6159	28000	0.5887
4.6983	28500	0.6031
4.7807	29000	0.5823
4.8632	29500	0.5953
4.9456	30000	0.5793

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.0.1
Transformers: 4.42.4
PyTorch: 2.3.1+cu121
Accelerate: 0.33.0
Datasets: 2.18.0
Tokenizers: 0.19.1

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: 5.12 tokens max: 30 tokens	min: 3 tokens mean: 9.06 tokens max: 56 tokens

sentence_0	sentence_1
`च𑀞𑀱च𑀢`	`च𑀞𑀱च𑀢 𑀭ठ𑀯`
`ठ𑀧𑀧𑁢𑀯`	`ठ𑀧𑀧𑁢𑀯`
`𑁢𑀗𑀯`	`𑁢𑀗𑀯`