Modernbert Large Msmarco Bpr
这是一个从ModernBERT-large微调的sentence-transformers模型,用于将句子和段落映射到1024维的密集向量空间,支持语义文本相似性、语义搜索等任务。
下载量 21
发布时间 : 2/7/2025
模型简介
该模型基于ModernBERT-large架构微调,专门用于句子和段落的向量表示,适用于多种自然语言处理任务。
模型特点
长文本处理能力
支持最大8192个标记的序列长度,适合处理长文档和段落。
高效向量表示
将文本映射到1024维的密集向量空间,保留丰富的语义信息。
微调优化
基于ModernBERT-large架构进行专门微调,优化了句子相似度任务的表现。
模型能力
语义文本相似度计算
语义搜索
释义挖掘
文本分类
文本聚类
使用案例
信息检索
相关文档检索
根据查询句子查找语义相似的相关文档段落
可有效匹配语义相关但表述不同的文本内容
问答系统
答案段落匹配
将用户问题与候选答案段落进行相似度匹配
可准确找到与问题最相关的答案段落
🚀 基于answerdotai/ModernBERT-large的句子转换器
这是一个基于 answerdotai/ModernBERT-large 微调的 sentence-transformers 模型。它可以将句子和段落映射到一个1024维的密集向量空间,可用于语义文本相似度计算、语义搜索、释义挖掘、文本分类、聚类等任务。
🚀 快速开始
本模型可以将句子和段落映射到一个1024维的密集向量空间,可用于语义文本相似度计算、语义搜索、释义挖掘、文本分类、聚类等任务。
✨ 主要特性
- 基于 answerdotai/ModernBERT-large 进行微调。
- 能够将句子和段落映射到1024维的密集向量空间。
- 可用于多种自然语言处理任务,如语义文本相似度计算、语义搜索等。
📦 安装指南
首先,你需要安装 Sentence Transformers 库:
pip install -U sentence-transformers
💻 使用示例
基础用法
from sentence_transformers import SentenceTransformer
# 从 🤗 Hub 下载模型
model = SentenceTransformer("BlackBeenie/ModernBERT-large-msmarco-bpr")
# 运行推理
sentences = [
'what is the average top third score on the act',
'North Dakota is among a dozen states where high school students are required to take the ACT before graduating. The state tied with Colorado for third with an average composite score of 20.6 this year. Utah was first with an average of 20.8 and Illinois was second at 20.7. ACT composite scores range from 1 to 36. The national average is 21.0. A total of 7,227 students in North Dakota took the ACT this year.',
"The average ACT score composite at Duke is a 34. The 25th percentile ACT score is 32, and the 75th percentile ACT score is 35. In other words, a 32 places you below average, while a 35 will move you up to above average.f you're a junior or senior, your GPA is hard to change from this point on. If your GPA is at or below the school average of 4.19, you'll need a higher ACT score to compensate and show that you're prepared to take on college academics.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# 获取嵌入向量的相似度分数
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
📚 详细文档
模型详情
模型描述
属性 | 详情 |
---|---|
模型类型 | 句子转换器 |
基础模型 | answerdotai/ModernBERT-large |
最大序列长度 | 8192个标记 |
输出维度 | 1024维 |
相似度函数 | 余弦相似度 |
模型来源
- 文档:Sentence Transformers 文档
- 仓库:GitHub 上的 Sentence Transformers
- Hugging Face:Hugging Face 上的 Sentence Transformers
完整模型架构
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(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})
)
训练详情
训练数据集
未命名数据集
- 大小:498,970 个训练样本
- 列:
sentence_0
、sentence_1
和sentence_2
- 基于前1000个样本的近似统计信息:
sentence_0 sentence_1 sentence_2 类型 字符串 字符串 字符串 详情 - 最小:4 个标记
- 平均:9.24 个标记
- 最大:27 个标记
- 最小:23 个标记
- 平均:83.71 个标记
- 最大:279 个标记
- 最小:17 个标记
- 平均:79.72 个标记
- 最大:262 个标记
- 样本:
sentence_0 sentence_1 sentence_2 what is tongkat ali
Tongkat Ali is a very powerful herb that acts as a sex enhancer by naturally increasing the testosterone levels, and revitalizing sexual impotence, performance and pleasure. Tongkat Ali is also effective in building muscular volume & strength resulting to a healthy physique.
However, unlike tongkat ali extract, tongkat ali chipped root and root powder are not sterile. Thus, the raw consumption of root powder is not recommended. The traditional preparation in Indonesia and Malaysia is to boil chipped roots as a tea. A standard dosage would be 50 gram of chipped root per person per day.
cost to install engineered hardwood flooring
Burton says his customers typically spend about $8 per square foot for engineered hardwood flooring; add an additional $2 per square foot for installation. Minion says consumers should expect to pay $7 to $12 per square foot for quality hardwood flooring. âIf the homeowner buys the wood and you need somebody to install it, usually an installation goes for about $2 a square foot,â Bill LeBeau, owner of LeBeauâs Hardwood Floors of Huntersville, North Carolina, says.
Installing hardwood flooring can cost between $9 and $12 per square foot, compared with about $3 to $5 per square foot for carpetâso some homeowners opt to install hardwood only in some rooms rather than throughout their home.However, carpet typically needs to be replaced if it becomes stained or worn out.ardwood flooring lasts longer than carpet, can be easier to keep clean and can be refinished. In the end, though, the decision about whether to install hardwood or carpeting in a bedroom should be based on your personal preference, at least if you intend to stay in the home for years.
define pollute
pollutes; polluted; polluting. Learner's definition of POLLUTE. [+ object] : to make (land, water, air, etc.) dirty and not safe or suitable to use. Waste from the factory had polluted [=contaminated] the river. Miles of beaches were polluted by the oil spill. Car exhaust pollutes the air.
Definition of pollute written for English Language Learners from the Merriam-Webster Learner's Dictionary with audio pronunciations, usage examples, and count/noncount noun labels. Learner's Dictionary mobile search
- 损失:
beir.losses.bpr_loss.BPRLoss
训练超参数
非默认超参数
eval_strategy
: stepsper_device_train_batch_size
: 32per_device_eval_batch_size
: 32num_train_epochs
: 5fp16
: Truemulti_dataset_batch_sampler
: round_robin
所有超参数
点击展开
overwrite_output_dir
: Falsedo_predict
: Falseeval_strategy
: stepsprediction_loss_only
: Trueper_device_train_batch_size
: 32per_device_eval_batch_size
: 32per_gpu_train_batch_size
: Noneper_gpu_eval_batch_size
: Nonegradient_accumulation_steps
: 1eval_accumulation_steps
: Nonetorch_empty_cache_steps
: Nonelearning_rate
: 5e-05weight_decay
: 0.0adam_beta1
: 0.9adam_beta2
: 0.999adam_epsilon
: 1e-08max_grad_norm
: 1num_train_epochs
: 5max_steps
: -1lr_scheduler_type
: linearlr_scheduler_kwargs
: {}warmup_ratio
: 0.0warmup_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}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
: round_robin
训练日志
点击展开
轮次 | 步骤 | 训练损失 |
---|---|---|
0.0321 | 500 | 1.517 |
0.0641 | 1000 | 0.355 |
0.0962 | 1500 | 0.3123 |
0.1283 | 2000 | 0.2916 |
0.1603 | 2500 | 0.2805 |
0.1924 | 3000 | 0.2782 |
0.2245 | 3500 | 0.2806 |
0.2565 | 4000 | 0.2831 |
0.2886 | 4500 | 0.2837 |
0.3207 | 5000 | 0.2603 |
0.3527 | 5500 | 0.2529 |
0.3848 | 6000 | 0.2681 |
0.4169 | 6500 | 0.2573 |
0.4489 | 7000 | 0.2678 |
0.4810 | 7500 | 0.2786 |
0.5131 | 8000 | 0.2559 |
0.5451 | 8500 | 0.2771 |
0.5772 | 9000 | 0.2807 |
0.6092 | 9500 | 0.2627 |
0.6413 | 10000 | 0.2536 |
0.6734 | 10500 | 0.2607 |
0.7054 | 11000 | 0.2578 |
0.7375 | 11500 | 0.2615 |
0.7696 | 12000 | 0.2624 |
0.8016 | 12500 | 0.2491 |
0.8337 | 13000 | 0.2487 |
0.8658 | 13500 | 0.2524 |
0.8978 | 14000 | 0.2465 |
0.9299 | 14500 | 0.2575 |
0.9620 | 15000 | 0.2412 |
0.9940 | 15500 | 0.2514 |
1.0 | 15593 | - |
1.0261 | 16000 | 0.1599 |
1.0582 | 16500 | 0.1495 |
1.0902 | 17000 | 0.1494 |
1.1223 | 17500 | 0.1437 |
1.1544 | 18000 | 0.1541 |
1.1864 | 18500 | 0.1455 |
1.2185 | 19000 | 0.1424 |
1.2506 | 19500 | 0.1456 |
1.2826 | 20000 | 0.1552 |
1.3147 | 20500 | 0.1508 |
1.3468 | 21000 | 0.1474 |
1.3788 | 21500 | 0.1534 |
1.4109 | 22000 | 0.1505 |
1.4430 | 22500 | 0.149 |
1.4750 | 23000 | 0.1616 |
1.5071 | 23500 | 0.1528 |
1.5392 | 24000 | 0.1531 |
1.5712 | 24500 | 0.151 |
1.6033 | 25000 | 0.1666 |
1.6353 | 25500 | 0.153 |
1.6674 | 26000 | 0.1532 |
1.6995 | 26500 | 0.1614 |
1.7315 | 27000 | 0.1576 |
1.7636 | 27500 | 0.154 |
1.7957 | 28000 | 0.1597 |
1.8277 | 28500 | 0.1512 |
1.8598 | 29000 | 0.1652 |
1.8919 | 29500 | 0.151 |
1.9239 | 30000 | 0.1561 |
1.9560 | 30500 | 0.1508 |
1.9881 | 31000 | 0.1463 |
2.0 | 31186 | - |
2.0201 | 31500 | 0.0999 |
2.0522 | 32000 | 0.0829 |
2.0843 | 32500 | 0.0799 |
2.1163 | 33000 | 0.0843 |
2.1484 | 33500 | 0.091 |
2.1805 | 34000 | 0.0843 |
2.2125 | 34500 | 0.092 |
2.2446 | 35000 | 0.0879 |
2.2767 | 35500 | 0.0914 |
2.3087 | 36000 | 0.092 |
2.3408 | 36500 | 0.101 |
2.3729 | 37000 | 0.1038 |
2.4049 | 37500 | 0.1084 |
2.4370 | 38000 | 0.0923 |
2.4691 | 38500 | 0.1083 |
2.5011 | 39000 | 0.0909 |
2.5332 | 39500 | 0.0918 |
2.5653 | 40000 | 0.101 |
2.5973 | 40500 | 0.0935 |
2.6294 | 41000 | 0.0858 |
2.6615 | 41500 | 0.0821 |
2.6935 | 42000 | 0.0755 |
2.7256 | 42500 | 0.0902 |
2.7576 | 43000 | 0.0906 |
2.7897 | 43500 | 0.089 |
2.8218 | 44000 | 0.088 |
2.8538 | 44500 | 0.0866 |
2.8859 | 45000 | 0.0914 |
2.9180 | 45500 | 0.0903 |
2.9500 | 46000 | 0.0903 |
2.9821 | 46500 | 0.0932 |
3.0 | 46779 | - |
3.0142 | 47000 | 0.0724 |
3.0462 | 47500 | 0.0465 |
3.0783 | 48000 | 0.049 |
3.1104 | 48500 | 0.0458 |
3.1424 | 49000 | 0.0461 |
3.1745 | 49500 | 0.0456 |
3.2066 | 50000 | 0.0469 |
3.2386 | 50500 | 0.051 |
3.2707 | 51000 | 0.044 |
3.3028 | 51500 | 0.0551 |
3.3348 | 52000 | 0.0549 |
3.3669 | 52500 | 0.0539 |
3.3990 | 53000 | 0.0515 |
3.4310 | 53500 | 0.0544 |
3.4631 | 54000 | 0.044 |
3.4952 | 54500 | 0.0499 |
3.5272 | 55000 | 0.0557 |
3.5593 | 55500 | 0.0571 |
3.5914 | 56000 | 0.0673 |
3.6234 | 56500 | 0.0512 |
3.6555 | 57000 | 0.0474 |
3.6876 | 57500 | 0.049 |
3.7196 | 58000 | 0.0552 |
3.7517 | 58500 | 0.046 |
3.7837 | 59000 | 0.0488 |
3.8158 | 59500 | 0.0477 |
3.8479 | 60000 | 0.054 |
3.8799 | 60500 | 0.0595 |
3.9120 | 61000 | 0.0462 |
3.9441 | 61500 | 0.0472 |
3.9761 | 62000 | 0.0553 |
4.0 | 62372 | - |
4.0082 | 62500 | 0.0438 |
4.0403 | 63000 | 0.0178 |
4.0723 | 63500 | 0.0187 |
4.1044 | 64000 | 0.0219 |
4.1365 | 64500 | 0.0254 |
4.1685 | 65000 | 0.0222 |
4.2006 | 65500 | 0.0229 |
4.2327 | 66000 | 0.0206 |
4.2647 | 66500 | 0.0195 |
4.2968 | 67000 | 0.0184 |
4.3289 | 67500 | 0.0224 |
4.3609 | 68000 | 0.019 |
4.3930 | 68500 | 0.0204 |
4.4251 | 69000 | 0.0187 |
4.4571 | 69500 | 0.0207 |
4.4892 | 70000 | 0.0215 |
4.5213 | 70500 | 0.0194 |
4.5533 | 71000 | 0.0206 |
4.5854 | 71500 | 0.0189 |
4.6175 | 72000 | 0.0222 |
4.6495 | 72500 | 0.0198 |
4.6816 | 73000 | 0.0199 |
4.7137 | 73500 | 0.0155 |
4.7457 | 74000 | 0.0185 |
4.7778 | 74500 | 0.0176 |
4.8099 | 75000 | 0.0181 |
4.8419 | 75500 | 0.0165 |
4.8740 | 76000 | 0.0204 |
4.9060 | 76500 | 0.0163 |
4.9381 | 77000 | 0.0154 |
4.9702 | 77500 | 0.0194 |
5.0 | 77965 | - |
框架版本
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
📄 许可证
文档中未提及相关信息。
📖 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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