moe-girl-test

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-test")
# Run inference
sentences = [
    '黑发、眼镜、外柔内刚、宅男、黑瞳、弱气',
    '角色：德怀特·费菲尔德\n本名：Dwight Fairfield\n别名：汪涵、领导、光头\n声优：Ian Chuprun\n发色：黑\n瞳色：黑\n身高：169\n萌点：眼镜、弱气、外柔内刚、上班族、宅男',
    '德怀特·费菲尔德',
]
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,  0.7818, -0.2186],
#         [ 0.7818,  1.0000, -0.0236],
#         [-0.2186, -0.0236,  1.0000]])

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

json

• Dataset: json
• Size: 30,620 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: 9 tokens</li><li>mean: 15.89 tokens</li><li>max: 30 tokens</li></ul>	<ul><li>min: 29 tokens</li><li>mean: 80.66 tokens</li><li>max: 347 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 7.39 tokens</li><li>max: 20 tokens</li></ul>

• Samples:

  anchor	positive	char_name
  <code>学姐、辣妹、绿瞳、及膝袜</code>	<code>角色：白石奈奈<br>本名：白石奈々<br>别名：学生会长、绚濑绘里、早坂爱、金发学姐<br>声优：M.A.O<br>发色：金<br>瞳色：绿<br>生日：5月30日<br>星座：双子<br>血型：O<br>萌点：辣妹、白丝、学姐</code>	<code>白石奈奈</code>
  <code>挑染、酒保、紫瞳</code>	<code>角色：陶陶<br>本名：陶陶<br>别名：陶宝<br>发色：紫<br>瞳色：紫<br>萌点：大学生、酒保、巨乳</code>	<code>陶陶</code>
  <code>紫瞳、粉发、乳袋</code>	<code>角色：美少女花骑士:蔓炎花<br>本名：マネッチア<br>别名：蔓炎花、火焰草、糖果玉米藤<br>发色：粉<br>瞳色：紫<br>萌点：魔、御姐、乳袋、船形帽、过膝袜、好酒、烧毁</code>	<code>美少女花骑士:蔓炎花</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: 3,403 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: 15.89 tokens</li><li>max: 31 tokens</li></ul>	<ul><li>min: 26 tokens</li><li>mean: 79.83 tokens</li><li>max: 276 tokens</li></ul>	<ul><li>min: 3 tokens</li><li>mean: 7.21 tokens</li><li>max: 22 tokens</li></ul>

• Samples:

  anchor	positive	char_name
  <code>御姐、蓝瞳、蓝发</code>	<code>角色：卡露娜<br>本名：カルナ<br>声优：柚木凉香<br>发色：蓝<br>瞳色：蓝<br>萌点：御姐、马尾</code>	<code>卡露娜</code>
  <code>露肩装、绿瞳、长发、卷发、科学家</code>	<code>角色：冷周六<br>发色：青<br>瞳色：浅绿<br>生日：12月23日<br>星座：摩羯<br>萌点：科学家、长发、卷发、露肩装、中靴</code>	<code>冷周六</code>
  <code>枪械、爸爸、A型、转轮手枪</code>	<code>角色：巴瑞·波顿<br>本名：バリー・バートン<br>别名：巴瑞<br>声优：Barry Gjerde、Ed Smaron、Jamieson K. Price、Michael McConnohie、屋良有作<br>发色：棕<br>瞳色：棕<br>年龄：38<br>血型：A<br>萌点：大叔、爸爸、手枪、转轮手枪、飞行员、特警、战术背心</code>	<code>巴瑞·波顿</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
• 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: 3
• 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: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• 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: 13.9 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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