SentenceTransformer based on google-bert/bert-base-uncased
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.
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
andsentence_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
: 16per_device_eval_batch_size
: 16num_train_epochs
: 5multi_dataset_batch_sampler
: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: noprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Falsefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Falsehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseeval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falsebatch_sampler
: batch_samplermulti_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
@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
@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",
}
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