---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:56574
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-small-zh-v1.5
widget:
- source_sentence: 围裙、大叔、爸爸、抽烟、上门女婿
sentences:
- Royde咖啡店老板
- '角色：罗丽娜
本名：罗丽娜
别名：萝莉娜
发色：黑
瞳色：红
萌点：萝莉、傲娇、孩子气'
- '角色：Royde咖啡店老板
别名：店长、老爸
发色：银
瞳色：琥珀
年龄：50
萌点：店长、大叔、爸爸、上门女婿、围裙、抽烟、妻管严'
- source_sentence: 流星锤、紫瞳、紫发、短裤、袜套
sentences:
- '角色：碧蓝航线:U-96
本名：U96
发色：白
瞳色：黄
萌点：双马尾、死库水、率真'
- '角色：科拉普斯
本名：コラプス、COLLAPSE、科拉普斯
声优：皆川纯子
发色：紫发黑
瞳色：紫
萌点：兔耳、正太、挑染、王冠、光环、露脐装、披风、短裤、外星人、星星眼、袜套、宠物相伴、流星锤'
- 科拉普斯
- source_sentence: 黑瞳、赛车手、短发
sentences:
- '角色：妮娜(JOJO的奇妙冒险)
本名：ネーナ、Nena、妮娜
别名：纯情女子
声优：雪野五月配音角色、竹内顺子配音角色、丰口惠美配音角色
发色：黑
瞳色：紫
年龄：16
萌点：耳环、项链、中分、头巾、眉心点、贵族、易容、本体差异型反差萌、褐色皮肤、手环、OVA）、美少女、单恋、麻花辫'
- 中村贤太
- '角色：中村贤太
本名：中村贤太
声优：冈野浩介、冈野浩介配音角色、成家义哉、成家义哉配音角色
发色：金发黑
瞳色：黑
萌点：短发、赛车手'
- source_sentence: 超能力者、绿瞳、旗袍
sentences:
- '角色：拳藤一佳
本名：拳藤、一佳
别名：拳藤大姐头、英雄名：战斗之拳
声优：小笠原早纪配音角色、凌晞配音角色、陈筱宁配音角色、连思宇配音角色
发色：橙
瞳色：绿
生日：9月9日
星座：处女
血型：O
萌点：侧马尾、班长、手刀、美少女、旗袍'
- '角色：厄克德娜
本名：エキドナ、Echidna
别名：旧文明的机神
声优：今谷皆美
发色：金
瞳色：绿
萌点：机娘、组织领导人'
- 拳藤一佳
- source_sentence: 熟女、A型、召唤能力者
sentences:
- '角色：永仓爱美琉
本名：永仓爱美琉、永仓、えみる
声优：前田爱
生日：7月20日
星座：巨蟹
三围：80、58、83
发色：橙
瞳色：棕
萌点：双马尾、玩偶、元气、冒失娘、记者、长筒袜、洋装、第三人称己称、发珠、中分、鬓角须'
- '角色：文豪野犬:尾崎红叶
本名：尾崎、红叶、Ozaki Kouyou
别名：红叶大姐、红叶君
声优：小清水亚美
发色：红
瞳色：红
生日：1月10日
星座：摩羯
血型：A
萌点：和服、遮眼发、超能力者、熟女'
- 文豪野犬:尾崎红叶
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on BAAI/bge-small-zh-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-zh-v1.5](https://huggingface.co/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](https://huggingface.co/BAAI/bge-small-zh-v1.5)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 512 dimensions
- **Similarity Function:** Cosine Similarity
- **Supported Modality:** Text
- **Training Dataset:**
- json
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("johnyy212/moe-girl-v2")
# Run inference
sentences = [
'熟女、A型、召唤能力者',
'角色：文豪野犬:尾崎红叶\n本名：尾崎、红叶、Ozaki Kouyou\n别名：红叶大姐、红叶君\n声优：小清水亚美\n发色：红\n瞳色：红\n生日：1月10日\n星座：摩羯\n血型：A\n萌点：和服、遮眼发、超能力者、熟女',
'文豪野犬:尾崎红叶',
]
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, 0.3878, -0.0819],
# [ 0.3878, 1.0000, 0.0236],
# [-0.0819, 0.0236, 1.0000]])
```
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 56,574 training samples
* Columns: anchor, positive, and char_name
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | char_name |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 4 tokens
- mean: 11.84 tokens
- max: 34 tokens
| - min: 12 tokens
- mean: 80.61 tokens
- max: 295 tokens
| - min: 3 tokens
- mean: 7.38 tokens
- max: 48 tokens
|
* Samples:
| anchor | positive | char_name |
|:---------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------|
| 井上和彦、早水理沙 | 角色：黑田
本名：黒田
别名：小黑、幼年时
声优：小野友树、小野友树配音角色、井上和彦、井上和彦配音角色、早水理沙、早水理沙配音角色
发色：黑
瞳色：灰蓝
萌点：现在、工程师、浴衣、绷带、遮眼发、烟斗、人偶师、大叔、毒舌、反差萌、贪财、心理创伤、青年时、幼驯染、搞事、天才、傲娇、腹黑、强气、双向暗恋、单马尾、天然疯、武士、蔷薇 | 黑田 |
| 平田裕香 | 角色：寺门卷子
本名：寺门 巻子
声优：平田裕香
萌点：同级生 | 寺门卷子 |
| 蝴蝶结、高跟鞋、麦克风、制服、伞、乙女 | 角色：瞳(LIVE A HERO)
本名：绫歌瞳
别名：仁美、爱抖露
声优：夏怜
发色：黑
瞳色：渐变
萌点：麦克风、乙女、歌手、偶像、暴力女、短发、丸子头、耳环、制服、伞、比基尼、外套半脱、手套、蝴蝶结、百褶裙、长筒袜、四分之三袜、高跟鞋、元气、光 | 瞳(LIVE A HERO) |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"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: anchor, positive, and char_name
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | char_name |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 9 tokens
- mean: 15.96 tokens
- max: 30 tokens
| - min: 30 tokens
- mean: 80.32 tokens
- max: 512 tokens
| - min: 3 tokens
- mean: 7.28 tokens
- max: 26 tokens
|
* Samples:
| anchor | positive | char_name |
|:-------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------|
| 小天使、发珠、料理达人、傲娇、长直 | 角色：云雀丘琉璃
本名：云雀丘瑠璃
别名：云雀、ヒバリ、本场琉璃
声优：白石晴香
发色：琉璃色黑
瞳色：绿
生日：7月9日
星座：巨蟹
血型：B
萌点：不幸、单恋、微傲娇、小天使、美少女、认真、人妻、料理达人、长直、吊眼、发珠、高跟鞋、绝对领域 | 云雀丘琉璃 |
| 黑瞳、银发、特工、B型 | 角色：风间让二
本名：风间、譲二
别名：二叔
声优：渡哲也配音角色
发色：灰
瞳色：黑
年龄：64
血型：B
萌点：特工、弟弟、长者、双胞胎 | 风间让二 |
| 红发、机郎、傲娇、蓝瞳 | 角色：爱因兹希
本名：爱因兹希
别名：全连结指挥体
声优：诹访部顺一
发色：红
瞳色：蓝
萌点：机郎、傲娇、基佬 | 爱因兹希 |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"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
Click to expand
- `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`: {'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`: {}