embbedding_text_1111 / README.md

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metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - dense
  - generated_from_trainer
  - dataset_size:50
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-large-zh-v1.5
widget:
  - source_sentence: 定期定額投資的優缺點
    sentences:
      - 近年來大型語言模型與擴散模型在圖像與文本生成領域取得突破性進展。
      - 國際間的生產與物流體系正在發生重大的組織變革與調整。
      - 透過固定金額長期投入，投資者能有效攤平市場波動帶來的成本風險，但可能在強勁牛市中錯失更高的單筆申購報酬。
  - source_sentence: 京都最適合賞楓的季節是什麼時候？
    sentences:
      - 秋季前往關西地區，十一月中旬到十二月初通常是觀賞紅葉的最佳時機。
      - 使用 asyncio 庫可以實現非阻塞的 I/O 操作，顯著提升網路爬蟲或 API 請求的並發性能。
      - 在快速變遷的職場環境中，持續獲取新知識與技能是維持個人競爭力與適應力的關鍵。
  - source_sentence: 長期失眠該如何改善？
    sentences:
      - 建立規律的作息時間、減少睡前使用電子產品，並營造舒適的睡眠環境有助於緩解睡眠障礙。
      - 植物透過葉綠體吸收太陽能，將二氧化碳與水轉化為葡萄糖並釋放氧氣，這是地球能量循環的基礎。
      - 辦理信用貸款通常要求穩定的收入證明與良好的信用評分。
  - source_sentence: 如何減少日常生活中的碳足跡
    sentences:
      - 在推動組織數位化過程中，往往會面臨技術債、員工抗拒改變以及缺乏清晰策略等難題。
      - 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。
      - 透過節能家電、搭乘大眾運輸及實踐蔬食生活，能有效降低個人的環境影響。
  - source_sentence: 京都最值得造訪的歷史古蹟
    sentences:
      - 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。
      - 這種以植物性食物、橄欖油和適量深海魚為主的飲食模式，被證實能有效預防心血管疾病。
datasets:
  - yenstdi/embbedding_text_1111
pipeline_tag: sentence-similarity
library_name: sentence-transformers

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

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.

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

	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:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

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

	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:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "gather_across_devices": false
}

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

@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{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}
}