Reasoning Bert Ccnews
這是一個基於BERT微調的句子轉換器模型,用於將句子和段落映射到768維向量空間,支持語義文本相似性、語義搜索等任務。
下載量 13
發布時間 : 3/28/2025
模型概述
該模型基於google-bert/bert-base-uncased微調,訓練數據集為reason_unfiltered,主要用於語義文本相似性計算、信息檢索等自然語言處理任務。
模型特點
高質量句子嵌入
將句子和段落映射到768維密集向量空間,保留語義信息
推理引導排序損失訓練
使用推理引導排序損失函數進行訓練,優化語義相似性判斷
多任務性能優化
在多個信息檢索任務上表現良好,包括nfcorpus、trec-covid等數據集
模型能力
語義文本相似性計算
語義搜索
釋義挖掘
文本分類
聚類分析
使用案例
信息檢索
文檔相似性搜索
在大規模文檔集合中查找語義相似的文檔
在nfcorpus數據集上達到0.5975的準確率@10
問答系統
匹配問題與候選答案的語義相似度
在quora數據集上達到0.7256的準確率@1
新聞分析
新聞內容相似性分析
分析不同新聞文章之間的語義關聯度
基於CCNews數據集訓練,適合新聞文本處理
🚀 基於google-bert/bert-base-uncased的句子轉換器模型
本模型是基於 google-bert/bert-base-uncased 在 reason_unfiltered 數據集上微調得到的 sentence-transformers 模型。它能將句子和段落映射到768維的密集向量空間,可用於語義文本相似度計算、語義搜索、釋義挖掘、文本分類、聚類等任務。
🚀 快速開始
安裝Sentence Transformers庫
pip install -U sentence-transformers
加載模型並進行推理
from sentence_transformers import SentenceTransformer
# 從🤗 Hub下載模型
model = SentenceTransformer("bwang0911/reasoning-bert-ccnews")
# 進行推理
sentences = [
'Energy advocates call for new commitment to renewable growth',
'The piece below was submitted by CFE, VoteSolar, and Environment Connecticut in response to the latest delay in the shared solar pilot program.\nSolar and environmental advocates are calling for a new community solar program in Connecticut that will expand solar access, energy choices and consumer savings for families, municipalities, and businesses statewide. The demand follows today’s Department of Energy and Environmental Protection (DEEP) technical hearing where attendees reviewed the state’s current Shared Clean Energy Facilities pilot program. The pilot has stalled several times over the last two years, most recently following DEEP’s decision to scrap all the proposals they have received and issue a new request for projects. DEEP heard from many advocates and developers at the hearing who are frustrated with this latest delay and skeptical about the long term success of the pilot.\nThe current pilot program was meant to expand solar access to Connecticut energy customers who can’t put solar on their own roof, but it contained flaws that have prevented any development to date. As set out in the legislation, the program has several poor design elements and a goal too small to draw significant private sector interest. Below are statements from stakeholders in Connecticut’s clean energy economy:\n“For years, Connecticut has missed out on the opportunity to bring solar energy choices to all consumers and more clean energy jobs to the state,” said Sean Garren, Northeast Regional director for Vote Solar. “Connecticut’s lackluster community solar program hasn’t unlocked the benefits of solar access for a single resident to date due to poor design and a lack of ambition at the scale needed, brought about by the electric utilities’ intervention. We’re calling on the legislature to catch up to the rest of New England — and the nation — with a smart, well-structured community solar program designed to serve consumers statewide.”\n“Two years of foot dragging and refusal by the Department of Energy and Environmental Protection to follow the law and implement a community solar program is preventing tens of thousands of Connecticut families from gaining access to clean, affordable, secure solar power,” said Chris Phelps, State Director for Environment Connecticut. “Community solar is helping other states accelerate solar growth, create jobs, and cut pollution. Connecticut policy makers should take action now to create a bold community solar program.”\n“Shared solar programs have been sweeping the nation for the last decade, but Connecticut has been left in the shade — losing out on healthier air, investment dollars, and green jobs that would accompany a full-scale, statewide shared solar program,” said Claire Coleman, Climate and Energy Attorney for Connecticut Fund for the Environment. “DEEP’s decision to start over with the already overly-restrictive shared solar pilot puts Connecticut further in the dark. Our climate and economy cannot wait any longer. Connecticut’s leaders must move quickly to ramp up in-state renewables through a full-scale shared solar program if Connecticut is going to have any chance of meeting its obligations under the Global Warming Solutions Act to reduce greenhouse gas emissions.”\nVote Solar is a nonprofit organization working to foster economic development and energy independence by bringing solar energy to the mainstream nationwide. Learn more at votesolar.org.',
"The second text elaborates on the first by providing details about the specific context of the energy advocates' call for renewable growth. It identifies the advocates (CFE, VoteSolar, Environment Connecticut), the specific renewable energy program (community solar), and the reasons for their call, including program delays and design flaws.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 獲取嵌入向量的相似度分數
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
✨ 主要特性
- 多任務支持:可用於語義文本相似度計算、語義搜索、釋義挖掘、文本分類、聚類等多種自然語言處理任務。
- 高維向量映射:能將句子和段落映射到768維的密集向量空間,便於進行語義分析。
- 微調優化:基於
google-bert/bert-base-uncased
模型在特定數據集上進行微調,提升了模型在相關任務上的性能。
📦 安裝指南
安裝Sentence Transformers庫:
pip install -U sentence-transformers
💻 使用示例
基礎用法
from sentence_transformers import SentenceTransformer
# 從🤗 Hub下載模型
model = SentenceTransformer("bwang0911/reasoning-bert-ccnews")
# 進行推理
sentences = [
'Energy advocates call for new commitment to renewable growth',
'The piece below was submitted by CFE, VoteSolar, and Environment Connecticut in response to the latest delay in the shared solar pilot program.\nSolar and environmental advocates are calling for a new community solar program in Connecticut that will expand solar access, energy choices and consumer savings for families, municipalities, and businesses statewide. The demand follows today’s Department of Energy and Environmental Protection (DEEP) technical hearing where attendees reviewed the state’s current Shared Clean Energy Facilities pilot program. The pilot has stalled several times over the last two years, most recently following DEEP’s decision to scrap all the proposals they have received and issue a new request for projects. DEEP heard from many advocates and developers at the hearing who are frustrated with this latest delay and skeptical about the long term success of the pilot.\nThe current pilot program was meant to expand solar access to Connecticut energy customers who can’t put solar on their own roof, but it contained flaws that have prevented any development to date. As set out in the legislation, the program has several poor design elements and a goal too small to draw significant private sector interest. Below are statements from stakeholders in Connecticut’s clean energy economy:\n“For years, Connecticut has missed out on the opportunity to bring solar energy choices to all consumers and more clean energy jobs to the state,” said Sean Garren, Northeast Regional director for VoteSolar. “Connecticut’s lackluster community solar program hasn’t unlocked the benefits of solar access for a single resident to date due to poor design and a lack of ambition at the scale needed, brought about by the electric utilities’ intervention. We’re calling on the legislature to catch up to the rest of New England — and the nation — with a smart, well-structured community solar program designed to serve consumers statewide.”\n“Two years of foot dragging and refusal by the Department of Energy and Environmental Protection to follow the law and implement a community solar program is preventing tens of thousands of Connecticut families from gaining access to clean, affordable, secure solar power,” said Chris Phelps, State Director for Environment Connecticut. “Community solar is helping other states accelerate solar growth, create jobs, and cut pollution. Connecticut policy makers should take action now to create a bold community solar program.”\n“Shared solar programs have been sweeping the nation for the last decade, but Connecticut has been left in the shade — losing out on healthier air, investment dollars, and green jobs that would accompany a full-scale, statewide shared solar program,” said Claire Coleman, Climate and Energy Attorney for Connecticut Fund for the Environment. “DEEP’s decision to start over with the already overly-restrictive shared solar pilot puts Connecticut further in the dark. Our climate and economy cannot wait any longer. Connecticut’s leaders must move quickly to ramp up in-state renewables through a full-scale shared solar program if Connecticut is going to have any chance of meeting its obligations under the Global Warming Solutions Act to reduce greenhouse gas emissions.”\nVote Solar is a nonprofit organization working to foster economic development and energy independence by bringing solar energy to the mainstream nationwide. Learn more at votesolar.org.',
"The second text elaborates on the first by providing details about the specific context of the energy advocates' call for renewable growth. It identifies the advocates (CFE, VoteSolar, Environment Connecticut), the specific renewable energy program (community solar), and the reasons for their call, including program delays and design flaws.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# 獲取嵌入向量的相似度分數
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
高級用法
# 高級場景說明:可根據具體需求對模型進行進一步的微調或與其他模型結合使用,以滿足更復雜的任務需求。
# 這裡假設我們有一個自定義的數據集,需要對模型進行微調
from sentence_transformers import SentenceTransformer, InputExample, losses
from torch.utils.data import DataLoader
# 加載預訓練模型
model = SentenceTransformer("bwang0911/reasoning-bert-ccnews")
# 自定義數據集
train_examples = [
InputExample(texts=['This is a sample sentence', 'Each sentence is converted'], label=0.8),
InputExample(texts=['This is another sample sentence', 'Each sentence is transformed'], label=0.7)
]
# 創建數據加載器
train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=2)
# 定義損失函數
train_loss = losses.CosineSimilarityLoss(model)
# 微調模型
model.fit(train_objectives=[(train_dataloader, train_loss)], epochs=1, warmup_steps=100)
# 使用微調後的模型進行推理
sentences = [
'This is a test sentence',
'This is a similar test sentence'
]
embeddings = model.encode(sentences)
print(embeddings.shape)
📚 詳細文檔
模型詳情
模型描述
屬性 | 詳情 |
---|---|
模型類型 | 句子轉換器 |
基礎模型 | google-bert/bert-base-uncased |
最大序列長度 | 196個詞元 |
輸出維度 | 768維 |
相似度函數 | 餘弦相似度 |
訓練數據集 | reason_unfiltered |
模型來源
- 文檔:Sentence Transformers Documentation
- 倉庫:Sentence Transformers on GitHub
- Hugging Face:Sentence Transformers on Hugging Face
完整模型架構
SentenceTransformer(
(0): Transformer({'max_seq_length': 196, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
評估指標
信息檢索
在 mteb/nfcorpus
、mteb/trec-covid
、mteb/fiqa
和 mteb/quora
數據集上進行評估,使用 InformationRetrievalEvaluator
進行評估。
指標 | mteb/nfcorpus | mteb/trec-covid | mteb/fiqa | mteb/quora |
---|---|---|---|---|
cosine_accuracy@1 | 0.3127 | 0.62 | 0.1373 | 0.7256 |
cosine_accuracy@3 | 0.4768 | 0.82 | 0.2284 | 0.8531 |
cosine_accuracy@5 | 0.5325 | 0.92 | 0.2701 | 0.8898 |
cosine_accuracy@10 | 0.5975 | 0.94 | 0.3457 | 0.9263 |
cosine_precision@1 | 0.3127 | 0.62 | 0.1373 | 0.7256 |
cosine_precision@3 | 0.2549 | 0.56 | 0.0931 | 0.3332 |
cosine_precision@5 | 0.2099 | 0.552 | 0.0694 | 0.2198 |
cosine_precision@10 | 0.1656 | 0.512 | 0.0465 | 0.1215 |
cosine_recall@1 | 0.0312 | 0.0005 | 0.0698 | 0.6303 |
cosine_recall@3 | 0.0562 | 0.0014 | 0.1265 | 0.79 |
cosine_recall@5 | 0.0688 | 0.0024 | 0.1566 | 0.8381 |
cosine_recall@10 | 0.097 | 0.0044 | 0.2 | 0.8875 |
cosine_ndcg@10 | 0.2185 | 0.5323 | 0.1575 | 0.8013 |
cosine_mrr@10 | 0.4016 | 0.7307 | 0.1957 | 0.796 |
cosine_map@100 | 0.0895 | 0.2299 | 0.1281 | 0.7648 |
訓練詳情
訓練數據集
- reason_unfiltered:reason_unfiltered 數據集,版本為 2e4fb05,包含44,978個訓練樣本。
- 數據集列:包含
title
、body
和reason
列。 - 近似統計信息:基於前1000個樣本的統計信息如下:
標題 正文 原因 類型 字符串 字符串 字符串 詳情 - 最小值:6個詞元
- 平均值:15.34個詞元
- 最大值:42個詞元
- 最小值:21個詞元
- 平均值:178.04個詞元
- 最大值:196個詞元
- 最小值:28個詞元
- 平均值:59.19個詞元
- 最大值:88個詞元
- 樣本示例:
標題 正文 原因 Fight Leaves Wayne Simmonds Shirtless
Reed Saxon/AP Images
Kevin Bieksa and Wayne Simmonds dropped the gloves just 95 seconds into last night’s 4-3 Ducks shootout win over the Flyers, and Bieksa immediately yanked his opponent’s jersey over his head, to the delight of the crowd and to grins from Simmonds and the officials.
That’s not supposed to happen. NHL players wear something called a fight strap, which binds the back of the jersey to the pants, preventing the jersey from being pulled off. (Losing a jersey is an advantage in a fight, as it gives the shirtless player’s opponent nothing to grab on to. Sabres enforcer Rob Ray was notorious for losing his gear in a fight, occasionally taking it off himself before clinching.) Any player who engaged in a fight without wearing a fight strap is subject to an automatic game misconduct.
Advertisement
Simmonds wasn’t ejected, though; at the one-minute mark of the video above, you can see he did have his fight strap properly attached. It just broke, which happens on occasion.The article describes a hockey fight involving Wayne Simmonds, confirming the title's claim. It details the fight, including Simmonds' jersey being pulled off, and explains the rules and context around the incident, directly elaborating on the event suggested by the title.
Merck CEO Kenneth Frazier ditches Trump over Charlottesville silence
Merck CEO Kenneth C. Frazier resigned from the president’s council on manufacturing Monday in direct protest of President Donald Trump’s lack of condemnation of white nationalist actions in Charlottesville, Va. over the weekend.
In a statement, Frazier, who is African-American, said he believes the country’s strength comes from the diversity of its citizens and that he feels personally compelled to stand up for that diversity and against intolerance.
“America’s leaders must honor our fundamental values by clearly rejecting expressions of hatred, bigotry and group supremacy, which run counter to the American ideal that all people are created equal,” he wrote. “As CEO of Merck, and as a matter of personal conscience, I feel a responsibility to take a stand against intolerance and extremism.”
RELATED: At least one death has been confirmed after a car plowed into a crowd of protesters in Charlottesville
Trump immediately fired back at Frazier on Twitter, saying the Merck CEO now “will have...The second text provides a detailed elaboration of the first. It explains the context of Kenneth Frazier's resignation, the reasons behind it (Trump's silence on Charlottesville), and includes Frazier's statement. It also provides additional background information about Frazier and the President's Manufacturing Council.
Lightning's Braydon Coburn: Joining road trip
Coburn (lower body) will travel with the team on its upcoming four-game road trip and is hoping to play at some point in the second half of the trip, Bryan Burns of the Lightning's official site reports.
The veteran blueliner is yet to play in the month of December, having already missed four games. However, the fact that Coburn is traveling with the team and has been given a chance to play at some point within the next week will be music to the ears of fantasy owners who benefited from Coburn's surprising production -- seven points in 25 games -- earlier in the season. Keep an eye out for updates as the trip progresses.The second text elaborates on the first by providing details about Braydon Coburn's situation. It specifies that he will join the team on a road trip and offers context about his injury, recovery timeline, and potential for playing, directly expanding on the initial announcement.
- 損失函數:使用
ReasoningGuidedRankingLoss
,參數如下:{ "scale": 20.0, "similarity_fct": "cos_sim" }
訓練超參數
- 非默認超參數:
eval_strategy
: stepsper_device_train_batch_size
: 256learning_rate
: 1e-05warmup_ratio
: 0.1fp16
: Truebatch_sampler
: no_duplicates
所有超參數
點擊展開
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 256per_device_eval_batch_size
: 8per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 1e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 3max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.1warmup_steps
: 0log_level
: passivelog_level_replica
: warninglog_on_each_node
: Truelogging_nan_inf_filter
: Truesave_safetensors
: Truesave_on_each_node
: Falsesave_only_model
: Falserestore_callback_states_from_checkpoint
: Falseno_cuda
: Falseuse_cpu
: Falseuse_mps_device
: Falseseed
: 42data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Falsefp16
: Truefp16_opt_level
: O1half_precision_backend
: autobf16_full_eval
: Falsefp16_full_eval
: Falsetf32
: Nonelocal_rank
: 0ddp_backend
: Nonetpu_num_cores
: Nonetpu_metrics_debug
: Falsedebug
: []dataloader_drop_last
: Falsedataloader_num_workers
: 0dataloader_prefetch_factor
: Nonepast_index
: -1disable_tqdm
: Falseremove_unused_columns
: Truelabel_names
: Noneload_best_model_at_end
: Falseignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}tp_size
: 0fsdp_transformer_layer_cls_to_wrap
: Noneaccelerator_config
: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed
: Nonelabel_smoothing_factor
: 0.0optim
: adamw_torchoptim_args
: Noneadafactor
: Falsegroup_by_length
: Falselength_column_name
: lengthddp_find_unused_parameters
: Noneddp_bucket_cap_mb
: Noneddp_broadcast_buffers
: Falsedataloader_pin_memory
: Truedataloader_persistent_workers
: Falseskip_memory_metrics
: Trueuse_legacy_prediction_loop
: Falsepush_to_hub
: Falseresume_from_checkpoint
: Nonehub_model_id
: Nonehub_strategy
: every_savehub_private_repo
: Nonehub_always_push
: Falsegradient_checkpointing
: Falsegradient_checkpointing_kwargs
: Noneinclude_inputs_for_metrics
: Falseinclude_for_metrics
: []eval_do_concat_batches
: Truefp16_backend
: autopush_to_hub_model_id
: Nonepush_to_hub_organization
: Nonemp_parameters
:auto_find_batch_size
: Falsefull_determinism
: Falsetorchdynamo
: Noneray_scope
: lastddp_timeout
: 1800torch_compile
: Falsetorch_compile_backend
: Nonetorch_compile_mode
: Nonedispatch_batches
: Nonesplit_batches
: Noneinclude_tokens_per_second
: Falseinclude_num_input_tokens_seen
: Falseneftune_noise_alpha
: Noneoptim_target_modules
: Nonebatch_eval_metrics
: Falseeval_on_start
: Falseuse_liger_kernel
: Falseeval_use_gather_object
: Falseaverage_tokens_across_devices
: Falseprompts
: Nonebatch_sampler
: no_duplicatesmulti_dataset_batch_sampler
: proportional
訓練日誌
輪次 | 步數 | 訓練損失 | mteb/nfcorpus_cosine_ndcg@10 | mteb/trec-covid_cosine_ndcg@10 | mteb/fiqa_cosine_ndcg@10 | mteb/quora_cosine_ndcg@10 |
---|---|---|---|---|---|---|
-1 | -1 | - | 0.0583 | 0.2174 | 0.0237 | 0.6103 |
0.0568 | 10 | 3.443 | - | - | - | - |
0.1136 | 20 | 2.9692 | - | - | - | - |
0.1705 | 30 | 2.1061 | - | - | - | - |
0.2273 | 40 | 1.3012 | 0.0901 | 0.3585 | 0.0642 | 0.7024 |
0.2841 | 50 | 0.9825 | - | - | - | - |
0.3409 | 60 | 0.7112 | - | - | - | - |
0.3977 | 70 | 0.5853 | - | - | - | - |
0.4545 | 80 | 0.5555 | 0.1714 | 0.5160 | 0.1287 | 0.7800 |
0.5114 | 90 | 0.4633 | - | - | - | - |
0.5682 | 100 | 0.4216 | - | - | - | - |
0.625 | 110 | 0.3846 | - | - | - | - |
0.6818 | 120 | 0.4017 | 0.1923 | 0.5446 | 0.1417 | 0.7890 |
0.7386 | 130 | 0.3606 | - | - | - | - |
0.7955 | 140 | 0.3731 | - | - | - | - |
0.8523 | 150 | 0.3451 | - | - | - | - |
0.9091 | 160 | 0.3352 | 0.2017 | 0.5343 | 0.1472 | 0.7951 |
0.9659 | 170 | 0.3364 | - | - | - | - |
1.0227 | 180 | 0.2606 | - | - | - | - |
1.0795 | 190 | 0.2627 | - | - | - | - |
1.1364 | 200 | 0.2641 | 0.2065 | 0.5449 | 0.1499 | 0.7963 |
1.1932 | 210 | 0.2448 | - | - | - | - |
1.25 | 220 | 0.2394 | - | - | - | - |
1.3068 | 230 | 0.2433 | - | - | - | - |
1.3636 | 240 | 0.2236 | 0.2096 | 0.5432 | 0.1519 | 0.7975 |
1.4205 | 250 | 0.221 | - | - | - | - |
1.4773 | 260 | 0.2215 | - | - | - | - |
1.5341 | 270 | 0.2291 | - | - | - | - |
1.5909 | 280 | 0.2433 | 0.2102 | 0.5322 | 0.1543 | 0.7994 |
1.6477 | 290 | 0.219 | - | - | - | - |
1.7045 | 300 | 0.2207 | - | - | - | - |
1.7614 | 310 | 0.2102 | - | - | - | - |
1.8182 | 320 | 0.2138 | 0.2163 | 0.5289 | 0.1553 | 0.8006 |
1.875 | 330 | 0.2076 | - | - | - | - |
1.9318 | 340 | 0.2076 | - | - | - | - |
1.9886 | 350 | 0.2066 | - | - | - | - |
2.0455 | 360 | 0.2046 | 0.2154 | 0.5339 | 0.1558 | 0.8006 |
2.1023 | 370 | 0.1844 | - | - | - | - |
2.1591 | 380 | 0.17 | - | - | - | - |
2.2159 | 390 | 0.1913 | - | - | - | - |
2.2727 | 400 | 0.165 | 0.2165 | 0.5339 | 0.1547 | 0.8014 |
2.3295 | 410 | 0.1878 | - | - | - | - |
2.3864 | 420 | 0.1841 | - | - | - | - |
2.4432 | 430 | 0.1683 | - | - | - | - |
2.5 | 440 | 0.1767 | 0.2178 | 0.5307 | 0.1565 | 0.8014 |
2.5568 | 450 | 0.1627 | - | - | - | - |
2.6136 | 460 | 0.161 | - | - | - | - |
2.6705 | 470 | 0.1717 | - | - | - | - |
2.7273 | 480 | 0.1832 | 0.2169 | 0.5341 | 0.1570 | 0.8012 |
2.7841 | 490 | 0.1673 | - | - | - | - |
2.8409 | 500 | 0.1517 | - | - | - | - |
2.8977 | 510 | 0.1797 | - | - | - | - |
2.9545 | 520 | 0.1862 | 0.2185 | 0.5323 | 0.1575 | 0.8013 |
框架版本
- Python: 3.10.12
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.50.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.4.1
- Tokenizers: 0.21.1
📄 許可證
文檔中未提及相關許可證信息。
📖 引用
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",
}
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