--- tags: - sentence-transformers - cross-encoder - reranker - generated_from_trainer - dataset_size:769572 - loss:BinaryCrossEntropyLoss base_model: BAAI/bge-reranker-v2-m3 pipeline_tag: text-ranking library_name: sentence-transformers metrics: - map - mrr@10 - ndcg@10 model-index: - name: CrossEncoder based on BAAI/bge-reranker-v2-m3 results: - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: query note test type: query_note_test metrics: - type: map value: 0.45654587062161356 name: Map - type: mrr@10 value: 0.5283685579312956 name: Mrr@10 - type: ndcg@10 value: 0.527108528024005 name: Ndcg@10 - task: type: cross-encoder-reranking name: Cross Encoder Reranking dataset: name: query note val type: query_note_val metrics: - type: map value: 0.45724051914340746 name: Map - type: mrr@10 value: 0.5246903296465979 name: Mrr@10 - type: ndcg@10 value: 0.5328170850876233 name: Ndcg@10 --- # CrossEncoder based on BAAI/bge-reranker-v2-m3 This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) using the [sentence-transformers](https://www.SBERT.net) 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](https://huggingface.co/BAAI/bge-reranker-v2-m3) - **Maximum Sequence Length:** 512 tokens - **Number of Output Labels:** 1 label - **Supported Modality:** Text ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) - **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder) ### 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: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python 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 * Datasets: `query_note_test` and `query_note_val` * Evaluated with [CrossEncoderRerankingEvaluator](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters: ```json { "at_k": 10 } ``` | 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 | | | | * 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](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters: ```json { "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 - [Training and Finetuning Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-reranker): the end-to-end guide for training or finetuning Cross Encoder (reranker) models. - [Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/multimodal-sentence-transformers): use text, image, audio, and video reranker models through the same API. - [Training and Finetuning Multimodal Embedding & Reranker Models with Sentence Transformers](https://huggingface.co/blog/train-multimodal-sentence-transformers): training multimodal Cross Encoders. ## Citation ### BibTeX #### Sentence Transformers ```bibtex @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", } ```