SentenceTransformer based on BAAI/bge-base-zh-v1.5

This is a sentence-transformers model finetuned from BAAI/bge-base-zh-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: BAAI/bge-base-zh-v1.5
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Supported Modality: Text

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'})
  (1): Pooling({'embedding_dimension': 768, 'pooling_mode': 'cls', 'include_prompt': True})
  (2): Normalize({})
)

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("liudev/novel-embedding-gpl-base-v1")
# Run inference
sentences = [
    '在异能觉醒爆发时安抚对方并带走宝物',
    '宴席上有人当众示爱引发全场震惊',
    '凶暴巨兽冲破防线引发骚乱',
    '山贼讲述当年的恐惧往事，结果再次遭遇强敌被瞬杀',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [4, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Training Details

Training Dataset

Unnamed Dataset

• Size: 435,262 training samples
• Columns: anchor, sentence1, sentence2, and label
• Approximate statistics based on the first 100 samples:

  	anchor	sentence1	sentence2	label
  type	string	string	string	float
  modality	text	text	text
  details	min: 8 tokensmean: 15.5 tokensmax: 25 tokens	min: 47 tokensmean: 73.19 tokensmax: 256 tokens	min: 29 tokensmean: 69.23 tokensmax: 92 tokens	min: 0.02mean: 0.37max: 0.84

• Samples:

  anchor	sentence1	sentence2	label
  看破敌人的隐身手段	周昂在围攻强敌时敏锐察觉到异样，通过观察灵气流动识破并破除隐身法术，最终果断出手将藏匿在墙头的施法者格杀。	李破在马场以赏罚分明的手段收拢人心并大摆酒宴后，在李碧的私下召见中，意外得知其欲随军出征辽东的计划并被征询意见。	0.759356061900818
  在仓库意外得到防身宝物	柳渊在医馆仓库意外获得防御法器佛珠及攻击真言，随后通过定星罗盘锁定义务星邪，并凭借缜密的推理引导葛老与辛捕头共同前往后院枯井探查。	沈河在烂尾楼工地通过系统指引接连开启宝箱，获得了具备强控效果的“大力飞砖”与防御性的“安全员安全帽”，在确认工地无剩余宝箱后，随即根据新坐标向十公里外进发。	0.09672797019024215
  进宫面圣前，太监好心提醒忌讳规矩	宦官郭穰在权衡利弊后决定拉拢入觐的天子待诏秀才张毅，不仅对其礼遇有加，更在入宫途中悉心传授面圣禁忌与生存经验，双方达成了初步的盟友共识。	在正月初一太子加冠的重大典礼上，南风费尽心思哄劝赖床的太子完成繁琐仪式并接手庞大的东宫官僚体系，同时担忧自己微服出游时的糗事在稍后的公开亮相中被路人认出。	0.5887360227245302

• Loss: MarginMSELoss with these parameters:

  {
      "similarity_fct": "pairwise_dot_score"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 32
• num_train_epochs: 150
• learning_rate: 2e-05
• warmup_steps: 0.02
• fp16: True
• push_to_hub: True
• hub_model_id: liudev/novel-embedding-gpl-base-v1
• hub_strategy: checkpoint
• dataloader_num_workers: 4

All Hyperparameters

• per_device_train_batch_size: 32
• num_train_epochs: 150
• max_steps: -1
• learning_rate: 2e-05
• lr_scheduler_type: linear
• lr_scheduler_kwargs: None
• warmup_steps: 0.02
• optim: adamw_torch_fused
• optim_args: None
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• optim_target_modules: None
• gradient_accumulation_steps: 1
• average_tokens_across_devices: True
• max_grad_norm: 1.0
• label_smoothing_factor: 0.0
• bf16: False
• fp16: True
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• use_liger_kernel: False
• liger_kernel_config: None
• use_cache: False
• neftune_noise_alpha: None
• torch_empty_cache_steps: None
• auto_find_batch_size: False
• log_on_each_node: True
• logging_nan_inf_filter: True
• include_num_input_tokens_seen: no
• log_level: passive
• log_level_replica: warning
• disable_tqdm: False
• project: huggingface
• trackio_space_id: None
• trackio_bucket_id: None
• trackio_static_space_id: None
• per_device_eval_batch_size: 8
• prediction_loss_only: True
• eval_on_start: False
• eval_do_concat_batches: True
• eval_use_gather_object: False
• eval_accumulation_steps: None
• include_for_metrics: []
• batch_eval_metrics: False
• save_only_model: False
• save_on_each_node: False
• enable_jit_checkpoint: False
• push_to_hub: True
• hub_private_repo: None
• hub_model_id: liudev/novel-embedding-gpl-base-v1
• hub_strategy: checkpoint
• hub_always_push: False
• hub_revision: None
• load_best_model_at_end: False
• ignore_data_skip: False
• restore_callback_states_from_checkpoint: False
• full_determinism: False
• seed: 42
• data_seed: None
• use_cpu: False
• 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
• dataloader_drop_last: True
• dataloader_num_workers: 4
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• dataloader_prefetch_factor: None
• dataloader_multiprocessing_context: None
• dataloader_in_order: True
• remove_unused_columns: True
• label_names: None
• train_sampling_strategy: random
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• ddp_static_graph: None
• ddp_backend: None
• ddp_timeout: 1800
• fsdp: None
• fsdp_config: None
• deepspeed: None
• debug: []
• skip_memory_metrics: True
• do_predict: False
• resume_from_checkpoint: None
• local_rank: -1
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional
• router_mapping: {}
• learning_rate_mapping: {}
• warmup_ratio: None

Training Logs

Training Time

• Training: 2.8 hours

Framework Versions

• Python: 3.13.15
• Sentence Transformers: 5.7.0
• Transformers: 5.16.1
• PyTorch: 2.11.0+cu128
• Accelerate: 1.14.0
• Datasets: 4.8.5
• Tokenizers: 0.23.1

Additional Resources

• Training and Finetuning Embedding Models with Sentence Transformers: the end-to-end guide for training or finetuning Sentence Transformer models.
• Introduction to Matryoshka Embedding Models: variable-size embeddings that can be truncated with minimal quality loss.
• Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval: post-training compression of embedding vectors.
• Multimodal Embedding & Reranker Models with Sentence Transformers: use text, image, audio, and video models through the same API.
• Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers: train multimodal embedding models, with a Visual Document Retrieval walkthrough.

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",
}

MarginMSELoss

@misc{hofstätter2021improving,
    title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
    author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
    year={2021},
    eprint={2010.02666},
    archivePrefix={arXiv},
    primaryClass={cs.IR}
}