Reranker Msmarco MiniLM L12 H384 Uncased Lambdaloss
這是一個基於MiniLM-L12-H384-uncased微調的交叉編碼器模型,用於文本重排序和語義搜索任務。
下載量 1,019
發布時間 : 3/14/2025
模型概述
該模型計算文本對的分數,主要用於文本重排序和語義搜索場景,能夠有效提升搜索結果的準確性。
模型特點
高效文本重排序
能夠快速計算文本對的相關性分數,適用於大規模搜索結果的重新排序
LambdaLoss優化
使用LambdaLoss損失函數訓練,優化了排序任務中的指標
輕量級模型
基於MiniLM架構,在保持性能的同時減少了模型大小和計算需求
模型能力
文本相關性評分
搜索結果重排序
語義搜索
問答系統排序
使用案例
信息檢索
搜索引擎結果優化
對搜索引擎返回的初步結果進行重新排序,提高相關結果排名
在NanoMSMARCO數據集上達到0.6352的平均精度
問答系統
答案候選排序
對問答系統生成的多個候選答案進行相關性排序
在NanoNQ數據集上達到0.7174的平均精度
🚀 基於microsoft/MiniLM-L12-H384-uncased的CrossEncoder
這是一個基於 microsoft/MiniLM-L12-H384-uncased 微調的 Cross Encoder 模型,使用了 sentence-transformers 庫。它可以計算文本對的得分,用於文本重排序和語義搜索。
✨ 主要特性
- 基於
microsoft/MiniLM-L12-H384-uncased
模型微調,適用於文本重排序和語義搜索任務。 - 支持使用
sentence-transformers
庫直接進行推理和微調。 - 提供了多種評估指標,如
map
、mrr@10
和ndcg@10
。
📦 安裝指南
首先,安裝 Sentence Transformers 庫:
pip install -U sentence-transformers
💻 使用示例
基礎用法
from sentence_transformers import CrossEncoder
# 從 🤗 Hub 下載模型
model = CrossEncoder("tomaarsen/reranker-msmarco-MiniLM-L12-H384-uncased-lambdaloss")
# 獲取文本對的得分
pairs = [
['一個雞蛋有多少卡路里', '一個雞蛋平均含有 55 到 80 卡路里,具體取決於其大小。'],
['一個雞蛋有多少卡路里', '雞蛋白的卡路里非常低,沒有脂肪,沒有膽固醇,並且富含蛋白質。'],
['一個雞蛋有多少卡路里', '雞蛋中的大部分卡路里來自中間的黃色蛋黃。'],
]
scores = model.predict(pairs)
print(scores.shape)
# (3,)
# 或者根據與單個文本的相似度對不同文本進行排序
ranks = model.rank(
'一個雞蛋有多少卡路里',
[
'一個雞蛋平均含有 55 到 80 卡路里,具體取決於其大小。',
'雞蛋白的卡路里非常低,沒有脂肪,沒有膽固醇,並且富含蛋白質。',
'雞蛋中的大部分卡路里來自中間的黃色蛋黃。',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
📚 詳細文檔
模型詳情
模型描述
屬性 | 詳情 |
---|---|
模型類型 | Cross Encoder |
基礎模型 | microsoft/MiniLM-L12-H384-uncased |
最大序列長度 | 512 個標記 |
輸出標籤數量 | 1 個標籤 |
模型來源
- 文檔:Sentence Transformers 文檔
- 文檔:Cross Encoder 文檔
- 倉庫:GitHub 上的 Sentence Transformers
- Hugging Face:Hugging Face 上的 Cross Encoders
評估
指標
Cross Encoder 重排序
- 數據集:
NanoMSMARCO_R100
、NanoNFCorpus_R100
和NanoNQ_R100
- 評估器:使用
CrossEncoderRerankingEvaluator
進行評估,參數如下:
{
"at_k": 10,
"always_rerank_positives": true
}
指標 | NanoMSMARCO_R100 | NanoNFCorpus_R100 | NanoNQ_R100 |
---|---|---|---|
map | 0.6352 (+0.1456) | 0.3389 (+0.0779) | 0.7174 (+0.2978) |
mrr@10 | 0.6298 (+0.1523) | 0.5872 (+0.0874) | 0.7283 (+0.3016) |
ndcg@10 | 0.6981 (+0.1577) | 0.4036 (+0.0786) | 0.7584 (+0.2577) |
Cross Encoder Nano BEIR
- 數據集:
NanoBEIR_R100_mean
- 評估器:使用
CrossEncoderNanoBEIREvaluator
進行評估,參數如下:
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
],
"rerank_k": 100,
"at_k": 10,
"always_rerank_positives": true
}
指標 | 值 |
---|---|
map | 0.5638 (+0.1738) |
mrr@10 | 0.6485 (+0.1805) |
ndcg@10 | 0.6200 (+0.1647) |
訓練詳情
訓練數據集
未命名數據集
- 大小:399,282 個訓練樣本
- 列:
query
、docs
和labels
- 近似統計信息(基於前 1000 個樣本):
| | 查詢 | 文檔 | 標籤 |
| ---- | ---- | ---- | ---- |
| 類型 | 字符串 | 列表 | 列表 |
| 詳情 |
- 最小:6 個字符
- 平均:33.0 個字符
- 最大:154 個字符
- 最小:6 個元素
- 平均:13.23 個元素
- 最大:20 個元素
- 最小:6 個元素
- 平均:13.23 個元素
- 最大:20 個元素
- 樣本:
| 查詢 | 文檔 | 標籤 |
| ---- | ---- | ---- |
|
intel current gen core processors
|["Identical or more capable versions of Core processors are also sold as Xeon processors for the server and workstation markets. As of 2017 the current lineup of Core processors included the Intel Core i7, Intel Core i5, and Intel Core i3, along with the Y - Series Intel Core CPU's.", "Most noticeably that Panasonic switched from Intel Core 2 Duo power to the latest Intel Core i3 and i5 processors. The three processors available in the new Toughbook 31, together with the new Mobile Intel QM57 Express chipset, are all part of Intel's Calpella platform.", 'The new 7th Gen Intel Core i7-7700HQ processor gives the 14-inch Razer Blade 2.8GHz of quad-core processing power and Turbo Boost speeds, which automatically increases the speed of active cores â\x80\x93 up to 3.8GHz.', 'Key difference: Intel Core i3 is a type of dual-core processor. i5 processors have 2 to 4 cores. A dual-core processor is a type of a central processing unit (CPU) that has two complete execution cores. Hence, it has t...
|[1, 0, 0, 0, 0, ...]
| |renovation definition
|['Renovation is the act of renewing or restoring something. If your kitchen is undergoing a renovation, thereâ\x80\x99s probably plaster and paint all over the place and you should probably get take-out.', 'NEW GALLERY SPACES OPENING IN 2017. In early 2017, our fourth floor will be transformed into a new destination for historical education and innovation. During the current renovation, objects from our permanent collection are on view throughout the Museum.', 'A same level house extension in Australia will cost approximately $60,000 to $200,000+. Adding a room or extending your living area on the ground floor are affordable ways of creating more space.Here are some key points to consider that will help you keep your renovation costs in check.RTICLE Stephanie Matheson. A same level house extension in Australia will cost approximately $60,000 to $200,000+. Adding a room or extending your living area on the ground floor are affordable ways of creating more space. Here are some key points...
|[1, 0, 0, 0, 0, ...]
| |what is a girasol
|['Girasol definition, an opal that reflects light in a bright luminous glow. See more.', 'Also, a type of opal from Mexico, referred to as Mexican water opal, is a colorless opal which exhibits either a bluish or golden internal sheen. Girasol opal is a term sometimes mistakenly and improperly used to refer to fire opals, as well as a type of transparent to semitransparent type milky quartz from Madagascar which displays an asterism, or star effect, when cut properly.', 'What is the meaning of Girasol? How popular is the baby name Girasol? Learn the origin and popularity plus how to pronounce Girasol', 'There are 5 basic types of opal. These types are Peruvian Opal, Fire Opal, Girasol Opal, Common opal and Precious Opal. There are 5 basic types of opal. These types are Peruvian Opal, Fire Opal, Girasol Opal, Common opal and Precious Opal.', 'girasol (Ë\x88dÊ\x92ɪrÉ\x99Ë\x8csÉ\x92l; -Ë\x8csÉ\x99Ê\x8al) , girosol or girasole n (Jewellery) a type of opal that has a red or pink glow in br...
|[1, 0, 0, 0, 0, ...]
| - 損失函數:使用
LambdaLoss
,參數如下:
{
"weighting_scheme": "sentence_transformers.cross_encoder.losses.LambdaLoss.NDCGLoss2PPScheme",
"k": null,
"sigma": 1.0,
"eps": 1e-10,
"reduction_log": "binary",
"activation_fct": "torch.nn.modules.linear.Identity",
"mini_batch_size": 16
}
評估數據集
未命名數據集
- 大小:1,000 個評估樣本
- 列:
query
、docs
和labels
- 近似統計信息(基於前 1000 個樣本):
| | 查詢 | 文檔 | 標籤 |
| ---- | ---- | ---- | ---- |
| 類型 | 字符串 | 列表 | 列表 |
| 詳情 |
- 最小:10 個字符
- 平均:33.63 個字符
- 最大:137 個字符
- 最小:3 個元素
- 平均:12.50 個元素
- 最大:20 個元素
- 最小:3 個元素
- 平均:12.50 個元素
- 最大:20 個元素
- 樣本:
| 查詢 | 文檔 | 標籤 |
| ---- | ---- | ---- |
|
can marijuana help dementia
|["Cannabis 'could stop dementia in its tracks'. Cannabis may help keep Alzheimer's disease at bay. In experiments, a marijuana-based medicine triggered the formation of new brain cells and cut inflammation linked to dementia. The researchers say that using the information to create a pill suitable for people could help prevent or delay the onset of Alzheimer's.", 'Marijuana (cannabis): Marijuana in any form is not allowed on aircraft and is not allowed in the secure part of the airport (beyond the TSA screening areas). In addition it is illegal to import marijuana or marijuana-related items into the US.', 'Depakote and dementia - Can dementia be cured? Unfortunately, no. Dementia is a progressive disease. Even available treatments only slow progression or tame symptoms.', 'Marijuana Prices. The price of marijuana listed below is the typical price to buy marijuana on the black market in U.S. dollars. How much marijuana cost and the sale price of marijuana are based upon the United Natio...
|[1, 0, 0, 0, 0, ...]
| |what are carcinogen
|['Written By: Carcinogen, any of a number of agents that can cause cancer in humans. They can be divided into three major categories: chemical carcinogens (including those from biological sources), physical carcinogens, and oncogenic (cancer-causing) viruses. 1 Most carcinogens, singly or in combination, produce cancer by interacting with DNA in cells and thereby interfering with normal cellular function.', 'Tarragon (Artemisia dracunculus) is a species of perennial herb in the sunflower family. It is widespread in the wild across much of Eurasia and North America, and is cultivated for culinary and medicinal purposes in many lands.One sub-species, Artemisia dracunculus var. sativa, is cultivated for use of the leaves as an aromatic culinary herb.arragon has an aromatic property reminiscent of anise, due to the presence of estragole, a known carcinogen and teratogen in mice. The European Union investigation revealed that the danger of estragole is minimal even at 100â\x80\x931,000 tim...
|[1, 0, 0, 0, 0, ...]
| |who played ben geller in friends
|["Noelle and Cali aren't the only twins to have played one child character in Friends. Double vision: Ross' cheeky son Ben (pictured), from his first marriage to Carol, was also played by twins, Dylan and Cole Sprouse, who are now 22.", 'Update 7/29/06: There are now three â\x80\x9cTeaching Pastorsâ\x80\x9d at Applegate Christian Fellowship, according to their web site. Jon Courson is now back at Applegate. The other two listed as Teaching Pastors are Jonâ\x80\x99s two sons: Peter John and Ben Courson.on Courson has been appreciated over the years by many people who are my friends and whom I respect. I believe that he preaches the real Jesus and the true Gospel, for which I rejoice. I also believe that his ministry and church organization is a reasonable example with which to examine important issues together.', 'Ben 10 (Reboot) Ben 10: Omniverse is the fourth iteration of the Ben 10 franchise, and it is the sequel of Ben 10: Ultimate Alien. Ben was all set to be a solo hero with his n...
|[1, 0, 0, 0, 0, ...]
| - 損失函數:使用
LambdaLoss
,參數如下:
{
"weighting_scheme": "sentence_transformers.cross_encoder.losses.LambdaLoss.NDCGLoss2PPScheme",
"k": null,
"sigma": 1.0,
"eps": 1e-10,
"reduction_log": "binary",
"activation_fct": "torch.nn.modules.linear.Identity",
"mini_batch_size": 16
}
訓練超參數
非默認超參數
eval_strategy
: stepsper_device_train_batch_size
: 16per_device_eval_batch_size
: 16learning_rate
: 2e-05num_train_epochs
: 1warmup_ratio
: 0.1seed
: 12bf16
: Trueload_best_model_at_end
: True
所有超參數
點擊展開
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 16per_device_eval_batch_size
: 16per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 2e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1.0num_train_epochs
: 1max_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
: 12data_seed
: Nonejit_mode_eval
: Falseuse_ipex
: Falsebf16
: Truefp16
: Falsefp16_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
: Trueignore_data_skip
: Falsefsdp
: []fsdp_min_num_params
: 0fsdp_config
: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_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
: batch_samplermulti_dataset_batch_sampler
: proportional
訓練日誌
點擊展開
輪次 | 步驟 | 訓練損失 | 驗證損失 | NanoMSMARCO_R100_ndcg@10 | NanoNFCorpus_R100_ndcg@10 | NanoNQ_R100_ndcg@10 | NanoBEIR_R100_mean_ndcg@10 |
---|---|---|---|---|---|---|---|
-1 | -1 | - | - | 0.0086 (-0.5318) | 0.2639 (-0.0612) | 0.0000 (-0.5006) | 0.0908 (-0.3645) |
0.0000 | 1 | 0.8912 | - | - | - | - | - |
0.0080 | 200 | 0.8246 | - | - | - | - | - |
0.0160 | 400 | 0.8117 | - | - | - | - | - |
0.0240 | 600 | 0.4376 | - | - | - | - | - |
0.0321 | 800 | 0.3271 | - | - | - | - | - |
0.0401 | 1000 | 0.2819 | 0.2442 | 0.6288 (+0.0884) | 0.4289 (+0.1039) | 0.7117 (+0.2111) | 0.5898 (+0.1344) |
0.0481 | 1200 | 0.24 | - | - | - | - | - |
0.0561 | 1400 | 0.2296 | - | - | - | - | - |
0.0641 | 1600 | 0.2244 | - | - | - | - | - |
0.0721 | 1800 | 0.2057 | - | - | - | - | - |
0.0801 | 2000 | 0.1947 | 0.1775 | 0.6251 (+0.0846) | 0.4318 (+0.1068) | 0.7111 (+0.2105) | 0.5893 (+0.1340) |
0.0882 | 2200 | 0.1939 | - | - | - | - | - |
0.0962 | 2400 | 0.1996 | - | - | - | - | - |
0.1042 | 2600 | 0.1938 | - | - | - | - | - |
0.1122 | 2800 | 0.1928 | - | - | - | - | - |
0.1202 | 3000 | 0.1915 | 0.1684 | 0.6226 (+0.0822) | 0.4140 (+0.0890) | 0.7063 (+0.2057) | 0.5810 (+0.1256) |
0.1282 | 3200 | 0.1898 | - | - | - | - | - |
0.1362 | 3400 | 0.1931 | - | - | - | - | - |
0.1443 | 3600 | 0.1834 | - | - | - | - | - |
0.1523 | 3800 | 0.1813 | - | - | - | - | - |
0.1603 | 4000 | 0.1722 | 0.1594 | 0.6584 (+0.1180) | 0.4167 (+0.0916) | 0.7031 (+0.2025) | 0.5927 (+0.1374) |
0.1683 | 4200 | 0.1759 | - | - | - | - | - |
0.1763 | 4400 | 0.187 | - | - | - | - | - |
0.1843 | 4600 | 0.1682 | - | - | - | - | - |
0.1923 | 4800 | 0.1813 | - | - | - | - | - |
0.2004 | 5000 | 0.1744 | 0.1541 | 0.6275 (+0.0871) | 0.4591 (+0.1341) | 0.7101 (+0.2095) | 0.5989 (+0.1435) |
0.2084 | 5200 | 0.164 | - | - | - | - | - |
0.2164 | 5400 | 0.1758 | - | - | - | - | - |
0.2244 | 5600 | 0.1715 | - | - | - | - | - |
0.2324 | 5800 | 0.1766 | - | - | - | - | - |
0.2404 | 6000 | 0.1633 | 0.1513 | 0.5947 (+0.0543) | 0.4002 (+0.0751) | 0.7161 (+0.2155) | 0.5703 (+0.1150) |
0.2484 | 6200 | 0.1675 | - | - | - | - | - |
0.2565 | 6400 | 0.1615 | - | - | - | - | - |
0.2645 | 6600 | 0.1697 | - | - | - | - | - |
0.2725 | 6800 | 0.1743 | - | - | - | - | - |
0.2805 | 7000 | 0.1781 | 0.1539 | 0.6461 (+0.1056) | 0.4281 (+0.1030) | 0.7288 (+0.2281) | 0.6010 (+0.1456) |
0.2885 | 7200 | 0.1796 | - | - | - | - | - |
0.2965 | 7400 | 0.1681 | - | - | - | - | - |
0.3045 | 7600 | 0.1746 | - | - | - | - | - |
0.3126 | 7800 | 0.1726 | - | - | - | - | - |
0.3206 | 8000 | 0.1625 | 0.1474 | 0.6162 (+0.0757) | 0.4363 (+0.1113) | 0.7352 (+0.2346) | 0.5959 (+0.1405) |
0.3286 | 8200 | 0.1574 | - | - | - | - | - |
0.3366 | 8400 | 0.1672 | - | - | - | - | - |
0.3446 | 8600 | 0.1766 | - | - | - | - | - |
0.3526 | 8800 | 0.1714 | - | - | - | - | - |
0.3606 | 9000 | 0.163 | 0.1497 | 0.6337 (+0.0933) | 0.4559 (+0.1308) | 0.7306 (+0.2300) | 0.6067 (+0.1513) |
0.3686 | 9200 | 0.1626 | - | - | - | - | - |
0.3767 | 9400 | 0.1638 | - | - | - | - | - |
0.3847 | 9600 | 0.1603 | - | - | - | - | - |
0.3927 | 9800 | 0.1689 | - | - | - | - | - |
0.4007 | 10000 | 0.1629 | 0.1500 | 0.6451 (+0.1046) | 0.4330 (+0.1080) | 0.7338 (+0.2332) | 0.6040 (+0.1486) |
0.4087 | 10200 | 0.1644 | - | - | - | - | - |
0.4167 | 10400 | 0.1596 | - | - | - | - | - |
0.4247 | 10600 | 0.1655 | - | - | - | - | - |
0.4328 | 10800 | 0.1596 | - | - | - | - | - |
0.4408 | 11000 | 0.1608 | 0.1416 | 0.6706 (+0.1302) | 0.4425 (+0.1174) | 0.7462 (+0.2455) | 0.6197 (+0.1644) |
0.4488 | 11200 | 0.1676 | - | - | - | - | - |
0.4568 | 11400 | 0.1642 | - | - | - | - | - |
0.4648 | 11600 | 0.1558 | - | - | - | - | - |
0.4728 | 11800 | 0.1582 | - | - | - | - | - |
0.4808 | 12000 | 0.1605 | 0.1471 | 0.6626 (+0.1222) | 0.4141 (+0.0890) | 0.7162 (+0.2156) | 0.5976 (+0.1423) |
0.4889 | 12200 | 0.1692 | - | - | - | - | - |
0.4969 | 12400 | 0.1592 | - | - | - | - | - |
0.5049 | 12600 | 0.1584 | - | - | - | - | - |
0.5129 | 12800 | 0.1613 | - | - | - | - | - |
0.5209 | 13000 | 0.1626 | 0.1436 | 0.6800 (+0.1396) | 0.4200 (+0.0949) | 0.7336 (+0.2329) | 0.6112 (+0.1558) |
0.5289 | 13200 | 0.1551 | - | - | - | - | - |
0.5369 | 13400 | 0.1622 | - | - | - | - | - |
0.5450 | 13600 | 0.1646 | - | - | - | - | - |
0.5530 | 13800 | 0.1642 | - | - | - | - | - |
0.5610 | 14000 | 0.1697 | 0.1396 | 0.6808 (+0.1403) | 0.4255 (+0.1005) | 0.7257 (+0.2250) | 0.6107 (+0.1553) |
0.5690 | 14200 | 0.1565 | - | - | - | - | - |
0.5770 | 14400 | 0.158 | - | - | - | - | - |
0.5850 | 14600 | 0.1497 | - | - | - | - | - |
0.5930 | 14800 | 0.1627 | - | - | - | - | - |
0.6011 | 15000 | 0.1599 | 0.1374 | 0.6647 (+0.1243) | 0.4185 (+0.0935) | 0.7465 (+0.2458) | 0.6099 (+0.1545) |
0.6091 | 15200 | 0.1586 | - | - | - | - | - |
0.6171 | 15400 | 0.1566 | - | - | - | - | - |
0.6251 | 15600 | 0.158 | - | - | - | - | - |
0.6331 | 15800 | 0.1693 | - | - | - | - | - |
0.6411 | 16000 | 0.157 | 0.1377 | 0.6844 (+0.1440) | 0.4022 (+0.0771) | 0.7715 (+0.2708) | 0.6193 (+0.1640) |
0.6491 | 16200 | 0.1508 | - | - | - | - | - |
0.6572 | 16400 | 0.1477 | - | - | - | - | - |
0.6652 | 16600 | 0.1589 | - | - | - | - | - |
0.6732 | 16800 | 0.148 | - | - | - | - | - |
0.6812 | 17000 | 0.153 | 0.1376 | 0.6835 (+0.1431) | 0.4230 (+0.0980) | 0.7471 (+0.2464) | 0.6179 (+0.1625) |
0.6892 | 17200 | 0.1599 | - | - | - | - | - |
0.6972 | 17400 | 0.152 | - | - | - | - | - |
0.7052 | 17600 | 0.1516 | - | - | - | - | - |
0.7133 | 17800 | 0.1537 | - | - | - | - | - |
0.7213 | 18000 | 0.1579 | 0.1386 | 0.6919 (+0.1514) | 0.4111 (+0.0860) | 0.7572 (+0.2565) | 0.6200 (+0.1646) |
0.7293 | 18200 | 0.1548 | - | - | - | - | - |
0.7373 | 18400 | 0.1492 | - | - | - | - | - |
0.7453 | 18600 | 0.1496 | - | - | - | - | - |
0.7533 | 18800 | 0.1514 | - | - | - | - | - |
0.7613 | 19000 | 0.1538 | 0.14 | 0.6981 (+0.1577) | 0.4036 (+0.0786) | 0.7584 (+0.2577) | 0.6200 (+0.1647) |
0.7694 | 19200 | 0.1504 | - | - | - | - | - |
0.7774 | 19400 | 0.146 | - | - | - | - | - |
0.7854 | 19600 | 0.1467 | - | - | - | - | - |
0.7934 | 19800 | 0.1542 | - | - | - | - | - |
0.8014 | 20000 | 0.1567 | 0.1365 | 0.6786 (+0.1382) | 0.4081 (+0.0831) | 0.7565 (+0.2559) | 0.6144 (+0.1591) |
0.8094 | 20200 | 0.1561 | - | - | - | - | - |
0.8174 | 20400 | 0.1444 | - | - | - | - | - |
0.8255 | 20600 | 0.15 | - | - | - | - | - |
0.8335 | 20800 | 0.1552 | - | - | - | - | - |
0.8415 | 21000 | 0.1548 | 0.1368 | 0.6786 (+0.1381) | 0.4111 (+0.0860) | 0.7544 (+0.2537) | 0.6147 (+0.1593) |
0.8495 | 21200 | 0.1533 | - | - | - | - | - |
0.8575 | 21400 | 0.1538 | - | - | - | - | - |
0.8655 | 21600 | 0.1486 | - | - | - | - | - |
0.8735 | 21800 | 0.1542 | - | - | - | - | - |
0.8816 | 22000 | 0.1536 | 0.1369 | 0.6670 (+0.1266) | 0.4102 (+0.0851) | 0.7504 (+0.2497) | 0.6092 (+0.1538) |
0.8896 | 22200 | 0.1604 | - | - | - | - | - |
0.8976 | 22400 | 0.1498 | - | - | - | - | - |
0.9056 | 22600 | 0.1563 | - | - | - | - | - |
0.9136 | 22800 | 0.154 | - | - | - | - | - |
0.9216 | 23000 | 0.1553 | 0.1363 | 0.6845 (+0.1441) | 0.4134 (+0.0884) | 0.7447 (+0.2441) | 0.6142 (+0.1589) |
0.9296 | 23200 | 0.1488 | - | - | - | - | - |
0.9377 | 23400 | 0.1489 | - | - | - | - | - |
0.9457 | 23600 | 0.1456 | - | - | - | - | - |
0.9537 | 23800 | 0.1561 | - | - | - | - | - |
0.9617 | 24000 | 0.1485 | 0.1374 | 0.6811 (+0.1407) | 0.4111 (+0.0861) | 0.7516 (+0.2510) | 0.6146 (+0.1592) |
0.9697 | 24200 | 0.1462 | - | - | - | - | - |
0.9777 | 24400 | 0.1472 | - | - | - | - | - |
0.9857 | 24600 | 0.1536 | - | - | - | - | - |
0.9937 | 24800 | 0.157 | - | - | - | - | - |
-1 | -1 | - | - | 0.6981 (+0.1577) | 0.4036 (+0.0786) | 0.7584 (+0.2577) | 0.6200 (+0.1647) |
- 加粗行表示保存的檢查點。
環境影響
使用 CodeCarbon 測量碳排放:
- 能源消耗:2.214 千瓦時
- 碳排放:0.861 千克二氧化碳
- 使用時長:7.301 小時
訓練硬件
- 是否使用雲服務:否
- GPU 型號:1 x NVIDIA GeForce RTX 3090
- CPU 型號:13th Gen Intel(R) Core(TM) i7-13700K
- 內存大小:31.78 GB
框架版本
- Python: 3.11.6
- Sentence Transformers: 3.5.0.dev0
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.4.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
📄 許可證
本模型使用 apache-2.0
許可證。
📚 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",
}
LambdaLoss
@inproceedings{wang2018lambdaloss,
title={The lambdaloss framework for ranking metric optimization},
author={Wang, Xuanhui and Li, Cheng and Golbandi, Nadav and Bendersky, Michael and Najork, Marc},
booktitle={Proceedings of the 27th ACM international conference on information and knowledge management},
pages={1313--1322},
year={2018}
}
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