This is a sentence-transformers model finetuned from BAAI/bge-large-zh-v1.5 on the embbedding_text_1111 dataset. It maps sentences & paragraphs to a 1024-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: BAAI/bge-large-zh-v1.5
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- embbedding_text_1111

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': True, 'architecture': 'BertModel'})
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (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.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    '京都最值得造訪的歷史古蹟',
    '這座日本古都擁有眾多世界文化遺產，如清水寺、金閣寺與伏見稻荷大社，是體驗傳統文化的必經之地。',
    '患者通常會感到胸口灼熱（俗稱火燒心）、胃酸逆流，有時還會伴隨慢性咳嗽或喉嚨發炎。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6704, 0.1423],
#         [0.6704, 1.0000, 0.1834],
#         [0.1423, 0.1834, 1.0000]])

Training Details

Training Dataset

embbedding_text_1111

Dataset: embbedding_text_1111 at 610ac14
Size: 50 training samples
Columns: anchor and positive
Approximate statistics based on the first 50 samples:
anchor positive
type string string
details
min: 11 tokens
mean: 16.02 tokens
max: 26 tokens
min: 26 tokens
mean: 39.0 tokens
max: 55 tokens

Samples:

anchor	positive
`尋找熟悉 React 生態系統的前端開發者`	`應徵者需具備 React, Redux 及 Next.js 的實作經驗，並能運用 TypeScript 撰寫高品質程式碼。`
`後端 Python 工程師職缺要求`	`精通 Django 或 FastAPI 框架，並有使用 Celery 處理非同步任務與分散式隊列的經驗。`
`雲端架構師 (AWS 專長) 招募中`	`負責維運 EC2、S3 與 Lambda 等雲端資源，並能有效配置 RDS 資料庫以確保系統效能。`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Evaluation Dataset

embbedding_text_1111

Dataset: embbedding_text_1111 at 610ac14
Size: 25 evaluation samples
Columns: anchor and positive
Approximate statistics based on the first 25 samples:
anchor positive
type string string
details
min: 11 tokens
mean: 15.16 tokens
max: 20 tokens
min: 28 tokens
mean: 39.36 tokens
max: 54 tokens

Samples:

anchor	positive
`這款手機的電池續航力令人印象深刻。`	`該行動裝置的電力持久度表現優異，能滿足長時間使用的需求。`
`什麼是機器學習中的過擬合現象？`	`當模型在訓練數據上表現極佳，但在未見過的測試數據上預測準確率大幅下降時，通常就是發生了 Overfitting。`
`2024年全球永續能源趨勢報告`	`隨著各國減碳政策的推進，太陽能與離岸風電在未來幾年將成為再生能源成長的核心動力。`

Loss: MultipleNegativesRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: epoch
per_device_train_batch_size: 16
per_device_eval_batch_size: 16
learning_rate: 2e-05
lr_scheduler_type: cosine
warmup_steps: 100

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: epoch
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
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: cosine
lr_scheduler_kwargs: None
warmup_ratio: 0.0
warmup_steps: 100
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
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}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch_fused
optim_args: None
adafactor: False
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
use_legacy_prediction_loop: False
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_inputs_for_metrics: False
include_for_metrics: []
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
include_tokens_per_second: False
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
prompts: None
batch_sampler: batch_sampler
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs

Epoch	Step	Training Loss	Validation Loss
0.25	1	1.3902	-
0.5	2	1.6712	-
0.75	3	1.2991	-
1.0	4	1.3125	0.1941
1.25	5	1.6758	-
1.5	6	1.5893	-
1.75	7	1.2746	-
2.0	8	0.0071	0.1854
2.25	9	1.236	-
2.5	10	1.0984	-
2.75	11	1.208	-
3.0	12	0.3278	0.1744

Framework Versions

Python: 3.12.12
Sentence Transformers: 5.2.0
Transformers: 4.57.6
PyTorch: 2.9.0+cu126
Accelerate: 1.12.0
Datasets: 4.0.0
Tokenizers: 0.22.2

Citation

BibTeX

Sentence Transformers

bibtex

@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

bibtex

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

	anchor	positive
type	string	string
details	min: 11 tokens mean: 16.02 tokens max: 26 tokens	min: 26 tokens mean: 39.0 tokens max: 55 tokens