johnyy212/moe-girl-v3

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

This is a sentence-transformers model finetuned from BAAI/bge-small-zh-v1.5 on the json dataset. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for retrieval.

Model Details

Model Description

• —Model Type: Sentence Transformer
• —Base model: BAAI/bge-small-zh-v1.5 <!-- at revision 7999e1d3359715c523056ef9478215996d62a620 -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 512 dimensions
• —Similarity Function: Cosine Similarity
• —Supported Modality: Text
• —Training Dataset:
• —json <!-- - Language: Unknown --> <!-- - License: Unknown -->

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': 512, '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("johnyy212/moe-girl-v3")
# Run inference
sentences = [
    '御姐、长短袜、下乳、长靴、红瞳',
    '角色：少女前线:PTRD\n本名：PTRD41反坦克步枪\n别名：卤蛋、南半球\n声优：清水爱\n萌点：长发、巨乳、黑丝、御姐、下乳、呆毛、哥萨克帽\n发色：亚麻\n瞳色：红',
    '少女前线:PTRD',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000,  0.5500, -0.1585],
#         [ 0.5500,  1.0000, -0.1041],
#         [-0.1585, -0.1041,  1.0000]])

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Downstream Usage (Sentence Transformers)

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Training Details

Training Dataset

json

• —Dataset: json
• —Size: 58,482 training samples
• —Columns: <code>anchor</code>, <code>positive</code>, and <code>char_name</code>
• —Approximate statistics based on the first 1000 samples: | | anchor | positive | char_name | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 3 tokens</li><li>mean: 11.96 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 77.78 tokens</li><li>max: 478 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.23 tokens</li><li>max: 23 tokens</li></ul> |
• —Samples: | anchor | positive | char_name | |:---------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------| | <code>杀手、吐槽、红发、混血儿</code> | <code>角色：风吹雪<br>本名：风吹雪<br>别名：雪妹、花、天意之花<br>年龄：16<br>瞳色：棕<br>发色：红<br>血型：O<br>萌点：长直、傲娇、女神系、吐槽、妹妹、裹胸布</code> | <code>风吹雪</code> | | <code>黑发、百合、高中生、半马尾、黑色连裤袜</code> | <code>角色：高远凛<br>本名：高远、凛<br>别名：凛、Rin<br>声优：长妻树里<br>生日：10月10日<br>瞳色：棕<br>发色：黑<br>三围：85、60、86<br>萌点：高中生、短发、穿越者、百合、黑丝、半马尾</code> | <code>高远凛</code> | | <code>小野凉子、北见六花</code> | <code>角色：世计丸<br>本名：世计、丸<br>声优：北见六花配音角色、小野凉子配音角色<br>发色：紫<br>瞳色：紫<br>三围：90、58、81<br>生日：12月1日<br>星座：射手<br>萌点：过膝袜、长发、下双马尾、毒舌、绝对领域、姐控、遮眼发、巨乳、傲娇</code> | <code>世计丸</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }

Evaluation Dataset

json

• —Dataset: json
• —Size: 3,615 evaluation samples
• —Columns: <code>anchor</code>, <code>positive</code>, and <code>char_name</code>
• —Approximate statistics based on the first 1000 samples: | | anchor | positive | char_name | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------| | type | string | string | string | | details | <ul><li>min: 9 tokens</li><li>mean: 15.88 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 77.17 tokens</li><li>max: 307 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 7.31 tokens</li><li>max: 24 tokens</li></ul> |
• —Samples: | anchor | positive | char_name | |:--------------------------------|:-------------------------------------------------------------------------------------------------------------|:-----------------------| | <code>萝莉控、黑色吊带袜、手套、绿瞳</code> | <code>角色：碧蓝航线:皇家方舟<br>本名：HMS Ark Royal<br>别名：狱友<br>发色：紫<br>瞳色：绿<br>萌点：御姐、黑色吊带袜、萝莉控、巨乳、遮眼发<br>生日：4月13日</code> | <code>碧蓝航线:皇家方舟</code> | | <code>孤儿、紫瞳、披风、长直、黑发、黑长直</code> | <code>角色：江昙<br>本名：江昙、Jiang Tan<br>别名：“圣女”、小昙花<br>声优：白杺瓒、皛四白、富田美忧<br>发色：黑<br>瞳色：紫<br>萌点：披风、针织衫、黑长直、孤儿</code> | <code>江昙</code> | | <code>尾巴、自大、嗜杀</code> | <code>角色：紫影狒狒<br>本名：Purprill、the、Mandroid、紫影狒狒<br>声优：高木涉<br>瞳色：绿<br>萌点：狒狒、尾巴、自大、嗜杀</code> | <code>紫影狒狒</code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim",
      "gather_across_devices": false,
      "directions": [
          "query_to_doc"
      ],
      "partition_mode": "joint",
      "hardness_mode": null,
      "hardness_strength": 0.0
  }

Training Hyperparameters

Non-Default Hyperparameters

• —per_device_train_batch_size: 64
• —learning_rate: 2e-05
• —num_train_epochs: 5
• —warmup_steps: 0.1
• —fp16: True
• —load_best_model_at_end: True
• —batch_sampler: no_duplicates

All Hyperparameters

<details><summary>Click to expand</summary>

• —do_predict: False
• —prediction_loss_only: True
• —per_device_train_batch_size: 64
• —per_device_eval_batch_size: 8
• —gradient_accumulation_steps: 1
• —eval_accumulation_steps: None
• —torch_empty_cache_steps: None
• —learning_rate: 2e-05
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —max_grad_norm: 1.0
• —num_train_epochs: 5
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: None
• —warmup_ratio: None
• —warmup_steps: 0.1
• —log_level: passive
• —log_level_replica: warning
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —enable_jit_checkpoint: False
• —save_on_each_node: False
• —save_only_model: False
• —restore_callback_states_from_checkpoint: False
• —use_cpu: False
• —seed: 42
• —data_seed: None
• —bf16: False
• —fp16: True
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —local_rank: -1
• —ddp_backend: None
• —debug: []
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_prefetch_factor: None
• —disable_tqdm: False
• —remove_unused_columns: True
• —label_names: None
• —load_best_model_at_end: True
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —parallelism_config: None
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamwtorchfused
• —optim_args: None
• —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
• —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_for_metrics: []
• —eval_do_concat_batches: True
• —auto_find_batch_size: False
• —full_determinism: False
• —ddp_timeout: 1800
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —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
• —use_cache: False
• —prompts: None
• —batch_sampler: no_duplicates
• —multi_dataset_batch_sampler: proportional
• —router_mapping: {}
• —learning_rate_mapping: {}

</details>

Training Logs

• —The bold row denotes the saved checkpoint.

Training Time

• —Training: 15.3 minutes

Framework Versions

• —Python: 3.12.13
• —Sentence Transformers: 5.4.1
• —Transformers: 5.0.0
• —PyTorch: 2.10.0+cu128
• —Accelerate: 1.13.0
• —Datasets: 4.8.5
• —Tokenizers: 0.22.2

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",
}

MultipleNegativesRankingLoss

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding},
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748},
}

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