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steerrec
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bge-reranker-v2-m3-query-note

Text Ranking
sentence-transformers
Safetensors
xlm-roberta
cross-encoder
reranker
Generated from Trainer
dataset_size:769572
loss:BinaryCrossEntropyLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community
How to use from the
Use from the
sentence-transformers library
from sentence_transformers import CrossEncoder

model = CrossEncoder("steerrec/bge-reranker-v2-m3-query-note")

query = "Which planet is known as the Red Planet?"
passages = [
	"Venus is often called Earth's twin because of its similar size and proximity.",
	"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
	"Jupiter, the largest planet in our solar system, has a prominent red spot.",
	"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
]

scores = model.predict([(query, passage) for passage in passages])
print(scores)

CrossEncoder based on BAAI/bge-reranker-v2-m3

This is a Cross Encoder model finetuned from BAAI/bge-reranker-v2-m3 using the sentence-transformers library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.

Model Details

Model Description

  • Model Type: Cross Encoder
  • Base model: BAAI/bge-reranker-v2-m3
  • Maximum Sequence Length: 512 tokens
  • Number of Output Labels: 1 label
  • Supported Modality: Text

Model Sources

Full Model Architecture 

CrossEncoder(
  (0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'XLMRobertaForSequenceClassification'})
)

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 CrossEncoder

# Download from the 🤗 Hub
model = CrossEncoder("steerrec/bge-reranker-v2-m3-query-note")
# Get scores for pairs of inputs
pairs = [
    ['infp男和infj女', '谁懂啊啊啊‼️💗INFP×INFJ相处好戳我\n绿老头和小蝴蝶都是高敏易碎人群，消息秒回啊，朋友圈点赞啊，这种细微且偏爱的行为，都能让对方感到安心和减少内耗。\n小蝴蝶的话，有事情就不要藏着掖着，对绿老头也不要有过多的试探行为，有问题就直接问，直接说，只要把事情拖到情绪都上来了，那这问题就更难解决了。因为绿老头只是看起来淡然，但其实他们的内心是很火热躁动的，他们只是不好意思主动表达。绿老头得学会调整，明知道小蝴蝶容易想多内耗，那就多主动表现情绪，不要让小蝴蝶觉得你对他淡淡的，要是能偶尔打个只球，明确的表达一下内心，那就更好了。#心理[话题]# #mbti[话题]# #infp[话题]# #infp日常[话题]# #infp和infj[话题]# #INFJ[话题]# #infj的世界[话题]# #我的日常[话题]# #温暖治愈[话题]# '],
    ['电动车远光灯刺眼反击', '电动车在这些情况下是全责哦！注意了哦！\n骑电动车一定要小心这些情况哦！[暗中观察R]#交通事故理赔[话题]# #交通事故[话题]# #法律求助[话题]# #电动车[话题]# #法律咨询[话题]# #交通安全[话题]# '],
    ['电动车远光灯刺眼反击', '支付宝上这个骑行险有用吗\n#车险[话题]# #电动车骑行险[话题]# #支付宝[话题]# '],
    ['牛奶品牌排名', '注意❗注意❗杯子到货，买就送📢\n杯子已经到货啦📣\n依然还是35.9到手2瓶4斤装的鲜奶，在额外赠送2个mini玻璃杯哦~\n顺丰冷链发货，包邮到家📦\n\xa0#乍甸牛奶[话题]#\xa0\xa0#云南游[话题]#\xa0\xa0#鲜奶[话题]#\xa0\xa0#可爱杯子[话题]#\xa0\xa0#杯子分享[话题]#\xa0\xa0#杯子控必入系列[话题]#\xa0\xa0#我就是个杯子控[话题]#\xa0\xa0#杯子[话题]#\xa0\xa0#鲜奶酸奶怎么挑[话题]#\xa0\xa0#鲜奶推荐[话题]#\xa0\xa0#鲜牛乳[话题]#\xa0\xa0#乍甸牛奶福利种草官[话题]#\xa0\xa0#乍甸牛奶也很好喝[话题]#\xa0\xa0#乍甸鲜奶[话题]#\xa0\t\n'],
    ['mbti人格', 'INFP小蝴蝶🦋请谨慎破防😅\n感觉本infp的确心灵上有些许脆弱，在面对朋友或者家人，别人的一句批评或者不认同，会影响我一天的心情～感觉这个习惯跟刻在骨子里一样，一边安慰自己，却一边焦虑😮\u200d💨很难想象有时候自己却很开朗乐观，其实内心很脆弱，一点就破⊙﹏⊙\n\t\n内容纯属娱乐🌚如有雷同纯属巧合🌚\n请大家对号入座哈哈哈哈😂#MBTI16型人格[话题]# #mbti梗图[话题]# #infp精神世界[话题]# #infp日常[话题]# '],
]
scores = model.predict(pairs)
print(scores)
# [0.3757 0.2134 0.1656 0.179  0.272 ]

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    'infp男和infj女',
    [
        '谁懂啊啊啊‼️💗INFP×INFJ相处好戳我\n绿老头和小蝴蝶都是高敏易碎人群，消息秒回啊，朋友圈点赞啊，这种细微且偏爱的行为，都能让对方感到安心和减少内耗。\n小蝴蝶的话，有事情就不要藏着掖着，对绿老头也不要有过多的试探行为，有问题就直接问，直接说，只要把事情拖到情绪都上来了，那这问题就更难解决了。因为绿老头只是看起来淡然，但其实他们的内心是很火热躁动的，他们只是不好意思主动表达。绿老头得学会调整，明知道小蝴蝶容易想多内耗，那就多主动表现情绪，不要让小蝴蝶觉得你对他淡淡的，要是能偶尔打个只球，明确的表达一下内心，那就更好了。#心理[话题]# #mbti[话题]# #infp[话题]# #infp日常[话题]# #infp和infj[话题]# #INFJ[话题]# #infj的世界[话题]# #我的日常[话题]# #温暖治愈[话题]# ',
        '电动车在这些情况下是全责哦！注意了哦！\n骑电动车一定要小心这些情况哦！[暗中观察R]#交通事故理赔[话题]# #交通事故[话题]# #法律求助[话题]# #电动车[话题]# #法律咨询[话题]# #交通安全[话题]# ',
        '支付宝上这个骑行险有用吗\n#车险[话题]# #电动车骑行险[话题]# #支付宝[话题]# ',
        '注意❗注意❗杯子到货，买就送📢\n杯子已经到货啦📣\n依然还是35.9到手2瓶4斤装的鲜奶，在额外赠送2个mini玻璃杯哦~\n顺丰冷链发货，包邮到家📦\n\xa0#乍甸牛奶[话题]#\xa0\xa0#云南游[话题]#\xa0\xa0#鲜奶[话题]#\xa0\xa0#可爱杯子[话题]#\xa0\xa0#杯子分享[话题]#\xa0\xa0#杯子控必入系列[话题]#\xa0\xa0#我就是个杯子控[话题]#\xa0\xa0#杯子[话题]#\xa0\xa0#鲜奶酸奶怎么挑[话题]#\xa0\xa0#鲜奶推荐[话题]#\xa0\xa0#鲜牛乳[话题]#\xa0\xa0#乍甸牛奶福利种草官[话题]#\xa0\xa0#乍甸牛奶也很好喝[话题]#\xa0\xa0#乍甸鲜奶[话题]#\xa0\t\n',
        'INFP小蝴蝶🦋请谨慎破防😅\n感觉本infp的确心灵上有些许脆弱，在面对朋友或者家人，别人的一句批评或者不认同，会影响我一天的心情～感觉这个习惯跟刻在骨子里一样，一边安慰自己，却一边焦虑😮\u200d💨很难想象有时候自己却很开朗乐观，其实内心很脆弱，一点就破⊙﹏⊙\n\t\n内容纯属娱乐🌚如有雷同纯属巧合🌚\n请大家对号入座哈哈哈哈😂#MBTI16型人格[话题]# #mbti梗图[话题]# #infp精神世界[话题]# #infp日常[话题]# ',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

Evaluation

Metrics

Cross Encoder Reranking

Metric query_note_test query_note_val
map 0.4565 0.4572
mrr@10 0.5284 0.5247
ndcg@10 0.5271 0.5328

Training Details

Training Dataset

Unnamed Dataset

  • Size: 769,572 training samples
  • Columns: sentence1, sentence2, and label
  • Approximate statistics based on the first 100 samples:
    sentence1 sentence2 label
    type string string float
    modality text text
    details
    • min: 5 tokens
    • mean: 7.92 tokens
    • max: 13 tokens
    • min: 18 tokens
    • mean: 208.42 tokens
    • max: 512 tokens
    • min: 0.0
    • mean: 0.0
    • max: 0.0
  • Samples:
    sentence1 sentence2 label
    infp男和infj女 谁懂啊啊啊‼️💗INFP×INFJ相处好戳我
    绿老头和小蝴蝶都是高敏易碎人群，消息秒回啊，朋友圈点赞啊，这种细微且偏爱的行为，都能让对方感到安心和减少内耗。
    小蝴蝶的话，有事情就不要藏着掖着，对绿老头也不要有过多的试探行为，有问题就直接问，直接说，只要把事情拖到情绪都上来了，那这问题就更难解决了。因为绿老头只是看起来淡然，但其实他们的内心是很火热躁动的，他们只是不好意思主动表达。绿老头得学会调整，明知道小蝴蝶容易想多内耗，那就多主动表现情绪，不要让小蝴蝶觉得你对他淡淡的，要是能偶尔打个只球，明确的表达一下内心，那就更好了。#心理[话题]# #mbti[话题]# #infp[话题]# #infp日常[话题]# #infp和infj[话题]# #INFJ[话题]# #infj的世界[话题]# #我的日常[话题]# #温暖治愈[话题]#     		0.0
    电动车远光灯刺眼反击 电动车在这些情况下是全责哦！注意了哦！
    骑电动车一定要小心这些情况哦！[暗中观察R]#交通事故理赔[话题]# #交通事故[话题]# #法律求助[话题]# #电动车[话题]# #法律咨询[话题]# #交通安全[话题]#     		0.0
    电动车远光灯刺眼反击 支付宝上这个骑行险有用吗
    #车险[话题]# #电动车骑行险[话题]# #支付宝[话题]#     		0.0
  • Loss: BinaryCrossEntropyLoss with these parameters:
    {
    "activation_fn": "torch.nn.modules.linear.Identity",
    "pos_weight": null
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 32
  • learning_rate: 0.0001
  • weight_decay: 0.01
  • num_train_epochs: 1
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.05
  • seed: 3407
  • bf16: True

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • prediction_loss_only: True
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 8
  • 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: 0.0001
  • weight_decay: 0.01
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 1
  • max_steps: -1
  • lr_scheduler_type: cosine
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.05
  • warmup_steps: 0
  • 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: 3407
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: True
  • 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
  • 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: False
  • 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: False
  • prompts: None
  • batch_sampler: batch_sampler
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Click to expand
Epoch Step Training Loss query_note_test_ndcg@10 query_note_val_ndcg@10
-1 -1 - 0.5206 -
0.7048 16950 0.6932 - -
0.7069 17000 0.5323 - -
0.7089 17050 0.5209 - -
0.7110 17100 0.5281 - -
0.7131 17150 0.5267 - -
0.7152 17200 0.5124 - -
0.7173 17250 0.5199 - -
0.7193 17300 0.5189 - -
0.7214 17350 0.5059 - -
0.7235 17400 0.5326 - -
0.7256 17450 0.5159 - -
0.7277 17500 0.5246 - -
0.7297 17550 0.5128 - -
0.7318 17600 0.5078 - -
0.7339 17650 0.4966 - -
0.7360 17700 0.506 - -
0.7380 17750 0.499 - -
0.7401 17800 0.5069 - -
0.7422 17850 0.5387 - -
0.7443 17900 0.5124 - -
0.7464 17950 0.522 - -
0.7484 18000 0.5103 - -
0.7505 18050 0.5217 - -
0.7526 18100 0.4939 - -
0.7547 18150 0.5151 - -
0.7568 18200 0.4804 - -
0.7588 18250 0.4969 - -
0.7609 18300 0.5277 - -
0.7630 18350 0.5143 - -
0.7651 18400 0.5063 - -
0.7672 18450 0.4899 - -
0.7692 18500 0.5144 - -
0.7713 18550 0.528 - -
0.7734 18600 0.5032 - -
0.7755 18650 0.4956 - -
0.7775 18700 0.5144 - -
0.7796 18750 0.5145 - -
0.7817 18800 0.4971 - -
0.7838 18850 0.5188 - -
0.7859 18900 0.501 - -
0.7879 18950 0.4892 - -
0.7900 19000 0.4752 - -
0.7921 19050 0.4984 - -
0.7942 19100 0.5001 - -
0.7963 19150 0.4809 - -
0.7983 19200 0.5085 - -
0.8004 19250 0.5122 - -
0.8025 19300 0.5122 - -
0.8046 19350 0.4909 - -
0.8067 19400 0.5341 - -
0.8087 19450 0.5147 - -
0.8108 19500 0.5095 - -
0.8129 19550 0.4945 - -
0.8150 19600 0.4971 - -
0.8170 19650 0.4967 - -
0.8191 19700 0.5108 - -
0.8212 19750 0.4983 - -
0.8233 19800 0.5154 - -
0.8254 19850 0.5214 - -
0.8274 19900 0.4953 - -
0.8295 19950 0.5079 - -
0.8316 20000 0.5252 - -
0.8337 20050 0.4966 - -
0.8358 20100 0.492 - -
0.8378 20150 0.5065 - -
0.8399 20200 0.4825 - -
0.8420 20250 0.4879 - -
0.8441 20300 0.5351 - -
0.8462 20350 0.4904 - -
0.8482 20400 0.5141 - -
0.8503 20450 0.5146 - -
0.8524 20500 0.508 - -
0.8545 20550 0.5271 - -
0.8565 20600 0.5057 - -
0.8586 20650 0.4757 - -
0.8607 20700 0.5151 - -
0.8628 20750 0.486 - -
0.8649 20800 0.4908 - -
0.8669 20850 0.5287 - -
0.8690 20900 0.5223 - -
0.8711 20950 0.5086 - -
0.8732 21000 0.5066 - -
0.8753 21050 0.5042 - -
0.8773 21100 0.5032 - -
0.8794 21150 0.5123 - -
0.8815 21200 0.4825 - -
0.8836 21250 0.5222 - -
0.8857 21300 0.5044 - -
0.8877 21350 0.5034 - -
0.8898 21400 0.5193 - -
0.8919 21450 0.4975 - -
0.8940 21500 0.4754 - -
0.8960 21550 0.5209 - -
0.8981 21600 0.5024 - -
0.9002 21650 0.5206 - -
0.9023 21700 0.5032 - -
0.9044 21750 0.5264 - -
0.9064 21800 0.499 - -
0.9085 21850 0.4967 - -
0.9106 21900 0.491 - -
0.9127 21950 0.5056 - -
0.9148 22000 0.4996 - -
0.9168 22050 0.4994 - -
0.9189 22100 0.5254 - -
0.9210 22150 0.5034 - -
0.9231 22200 0.5123 - -
0.9252 22250 0.4956 - -
0.9272 22300 0.5194 - -
0.9293 22350 0.474 - -
0.9314 22400 0.4842 - -
0.9335 22450 0.4914 - -
0.9356 22500 0.4925 - -
0.9376 22550 0.4938 - -
0.9397 22600 0.5086 - -
0.9418 22650 0.457 - -
0.9439 22700 0.5185 - -
0.9459 22750 0.5268 - -
0.9480 22800 0.4872 - -
0.9501 22850 0.5048 - -
0.9522 22900 0.5103 - -
0.9543 22950 0.5236 - -
0.9563 23000 0.5049 - -
0.9584 23050 0.5041 - -
0.9605 23100 0.5066 - -
0.9626 23150 0.5206 - -
0.9647 23200 0.4732 - -
0.9667 23250 0.4881 - -
0.9688 23300 0.5099 - -
0.9709 23350 0.5226 - -
0.9730 23400 0.5322 - -
0.9751 23450 0.4993 - -
0.9771 23500 0.4856 - -
0.9792 23550 0.4727 - -
0.9813 23600 0.5093 - -
0.9834 23650 0.5073 - -
0.9854 23700 0.5153 - -
0.9875 23750 0.4979 - -
0.9896 23800 0.4961 - -
0.9917 23850 0.5093 - -
0.9938 23900 0.4811 - -
0.9958 23950 0.5008 - -
0.9979 24000 0.5151 - -
1.0 24050 0.5318 - 0.5328
-1 -1 - 0.5271 -

Training Time

  • Training: 57.2 minutes

Framework Versions

  • Python: 3.12.3
  • Sentence Transformers: 5.5.1
  • Transformers: 4.56.2
  • PyTorch: 2.11.0+cu128
  • Accelerate: 1.13.0
  • Datasets: 4.3.0
  • Tokenizers: 0.22.2

Additional Resources

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",
}
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