T-Blue/tsdae_pro_text2vec

SentenceTransformer based on shibing624/text2vec-base-multilingual

This is a sentence-transformers model finetuned from shibing624/text2vec-base-multilingual. It maps sentences & paragraphs to a 384-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: shibing624/text2vec-base-multilingual
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 384 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': 384, '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_text2vec")
# Run inference
sentences = [
    '𑀠चपच𑀞𑀢𑀟 पच𑀟च ढनबच 𑀱च 𑀟च𑀠ध𑁣ल लच𑀣𑀢𑁦𑀳 𑀲त पच 𑀱च𑀳च𑀯',
    ' च 𑀠चपच𑀞𑀢𑀟 𑀞नल𑁣ढ पच𑀟च ढनबच 𑀱च 𑀞𑁣𑀠च𑀳 𑀟च𑀠ध𑁣ल लच𑀣𑀢𑁦𑀳 𑀲त पच 𑀟च𑀠𑀢ढ𑀢च 𑀱च𑀳च𑀯',
    ' णच𑀟𑀞न𑀟च𑀟 बन𑀟𑀣न𑀠च𑀪 𑀘𑀣𑁦ण𑀣𑁦𑀫 ब𑀢𑀣च 𑀟𑁦 बच ब𑀢𑀣च𑀘𑁦 𑀠च𑀳न णच𑀱च 𑀟च झच𑀪𑀟𑀢 𑀟च 𑀭𑁢 𑀣च 𑀟च 𑀭𑀬 𑀟च चल𑁦धध𑀢𑀟 ढ𑁣न𑀪ब𑁦𑁣𑀢𑀳𑀢𑁦𑀦 𑀱चञच𑀟𑀣च 𑀞𑁦 ञचन𑀞𑁦 𑀣च 𑀤च𑀟𑁦𑀟 𑀣नप𑀳𑁦𑀯',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

• Size: 64,000 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: 37.42 tokens max: 342 tokens	min: 4 tokens mean: 89.84 tokens max: 512 tokens

• Samples:

  sentence_0	sentence_1
  𑀠नपच𑀟𑁦𑀫च𑀢𑀫न𑀱च𑀟 𑀭थथ𑀬𑀯	𑀞𑀢𑀣𑀢𑀣𑀣𑀢बच𑀪 𑀳च𑀟च𑀙च𑀞नल𑁣ढझच𑀳च𑀳𑀫𑁦𑀟 𑀣न𑀟𑀢णच𑀠च𑀟च𑀤च𑀪पच 𑀪चणचणणन𑀟 𑀠नपच𑀟𑁦𑀫च𑀢𑀫न𑀱च𑀟 𑀭थथ𑀬𑀯
  च 𑀱च𑀘𑁦𑀟 𑀘च𑀠भ𑀢णणच 𑀠च𑀢 𑀞𑀢𑀳𑀫𑀢𑀟 पच बच𑀳𑀞𑀢णच𑀯	𑀘च𑀠भ𑀢णणच𑀪 च ल𑁣𑀞चत𑀢𑀟 𑀢पच त𑁦 पच ढ𑀢णन 𑀣च पच ण𑀢 𑀟च𑀠𑀢𑀘𑀢𑀟 𑀞𑁣𑀞च𑀪𑀢 𑀱च𑀘𑁦𑀟 𑀳च𑀠च𑀪 𑀣च 𑀘च𑀠भ𑀢णणच 𑀠च𑀢 𑀞𑀢𑀳𑀫𑀢𑀟 𑀞च𑀳च पच बच𑀳𑀞𑀢णच𑀯
  𑀯	𑀯

• Loss: DenoisingAutoEncoderLoss

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 16
• per_device_eval_batch_size: 16
• 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: 3
• 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
0.125	500	4.0592
0.25	1000	1.6454
0.375	1500	1.4774
0.5	2000	1.4131
0.625	2500	1.3766
0.75	3000	1.3488
0.875	3500	1.3252
1.0	4000	1.3087
1.125	4500	1.2931
1.25	5000	1.2772
1.375	5500	1.2655
1.5	6000	1.2535
1.625	6500	1.243
1.75	7000	1.2305
1.875	7500	1.223
2.0	8000	1.216
2.125	8500	1.2073
2.25	9000	1.1999
2.375	9500	1.1935
2.5	10000	1.1872
2.625	10500	1.1804
2.75	11000	1.17
2.875	11500	1.167
3.0	12000	1.1623

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",
}