ft-bge-reranker-v2-m3-test

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 <!-- at revision 953dc6f6f85a1b2dbfca4c34a2796e7dde08d41e -->
• Maximum Sequence Length: 8192 tokens
• Number of Output Labels: 1 label

<!-- - **Training Dataset:** Unknown --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Documentation: Cross Encoder Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Cross Encoders on Hugging Face

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("cross_encoder_model_id")
# Get scores for pairs of texts
pairs = [
    ["What is the significance of Samsung Electronics as a Korean brand in the list of the world's top 100 trademarks?", '由于其正处于产品开发与验证投入阶段，影响了公司的投资收益。\n\n\u3000\u3000设备企业：\n\n\n\u3000\u3000业绩翻倍增长\n\n\u3000\u3000虽然整体半导体板块尚未走出低谷，但国产替代需求推动下，设备环节企业保持逆周期高速增长，龙头设备厂商上半年业绩翻倍增长。国家统计局最新披露，围绕着克服“卡脖子”工程，今年上半年半导体相关行业制造业增长较快，半导体器件专用设备制造业增加值增长30.9%。'],
    ['根据文中提到的上游、中游和下游的不同环节，请简要描述半导体产业链的整体结构。', 'DRAM市场由三星、美光、海力士垄断了95%的份额，目前国产厂商合肥长鑫已经开始量产并在官网上架了相关产品，紫光集团也已建立DRAM事业部准备建厂。\n\nNAND Flash的市场由三星、西数、铠侠等6家企业垄断。目前NAND Flash的发展方向是3D堆叠，国外先进企业均已纷纷开发出100层以上堆叠的NAND Flash。国产厂商长江存储已宣布128层产品研发成功，与国外先进企业的差距越来越小，已成为存储国产自主化的中坚力量。'],
    ['根据上下文信息，提出一个问题。', '半导体材料是制作晶体管、集成电路、光电子器件的重要材料。\n\n按照化学组成不同，半导体材料可以分为元素半导体和化合物半导体两大类。'],
    ['What is the projected annual growth rate of the automotive semiconductor market from 2013 to 2018 according to IHS data?', '长电科技作为A股半导体封装测试龙头，第二季度业绩也环比大幅增长。业绩预告显示，今年上半年公司实现归母净利润为4.46亿元到5.46亿元，同比减少64.65%到71.08%。公司一季度实现归母净利润约1.1亿元，第二季度或实现盈利3.36亿至4.36亿元，环比一季度增长约两倍以上，公司不断投入汽车电子、工业电子及高性能计算等领域，为新一轮应用需求增长做好准备。此前，长电科技介绍，面向高算力芯片公司推出了Chiplet高性能封装技术平台XDFOI。'],
    ['你认为人工智能未来可能在哪些领域发挥作用？', '98亿元，其中，当期汇兑损失造成净利润减少约2.03亿元，剔除该因素，上半年公司净利润为正。通富微电介绍，全球半导体市场疲软,下游需求复苏不及预期，导致封测环节业务承压,公司传统业务亦受到较大影响。作为应对，公司调整产品布局，在高性能计算、新能源、汽车电子、存储、显示驱动等领域实现营收增长，积极推动Chiplet（芯粒）市场化应用,实现了规模性量产。'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)

# Or rank different texts based on similarity to a single text
ranks = model.rank(
    "What is the significance of Samsung Electronics as a Korean brand in the list of the world's top 100 trademarks?",
    [
        '由于其正处于产品开发与验证投入阶段，影响了公司的投资收益。\n\n\u3000\u3000设备企业：\n\n\n\u3000\u3000业绩翻倍增长\n\n\u3000\u3000虽然整体半导体板块尚未走出低谷，但国产替代需求推动下，设备环节企业保持逆周期高速增长，龙头设备厂商上半年业绩翻倍增长。国家统计局最新披露，围绕着克服“卡脖子”工程，今年上半年半导体相关行业制造业增长较快，半导体器件专用设备制造业增加值增长30.9%。',
        'DRAM市场由三星、美光、海力士垄断了95%的份额，目前国产厂商合肥长鑫已经开始量产并在官网上架了相关产品，紫光集团也已建立DRAM事业部准备建厂。\n\nNAND Flash的市场由三星、西数、铠侠等6家企业垄断。目前NAND Flash的发展方向是3D堆叠，国外先进企业均已纷纷开发出100层以上堆叠的NAND Flash。国产厂商长江存储已宣布128层产品研发成功，与国外先进企业的差距越来越小，已成为存储国产自主化的中坚力量。',
        '半导体材料是制作晶体管、集成电路、光电子器件的重要材料。\n\n按照化学组成不同，半导体材料可以分为元素半导体和化合物半导体两大类。',
        '长电科技作为A股半导体封装测试龙头，第二季度业绩也环比大幅增长。业绩预告显示，今年上半年公司实现归母净利润为4.46亿元到5.46亿元，同比减少64.65%到71.08%。公司一季度实现归母净利润约1.1亿元，第二季度或实现盈利3.36亿至4.36亿元，环比一季度增长约两倍以上，公司不断投入汽车电子、工业电子及高性能计算等领域，为新一轮应用需求增长做好准备。此前，长电科技介绍，面向高算力芯片公司推出了Chiplet高性能封装技术平台XDFOI。',
        '98亿元，其中，当期汇兑损失造成净利润减少约2.03亿元，剔除该因素，上半年公司净利润为正。通富微电介绍，全球半导体市场疲软,下游需求复苏不及预期，导致封测环节业务承压,公司传统业务亦受到较大影响。作为应对，公司调整产品布局，在高性能计算、新能源、汽车电子、存储、显示驱动等领域实现营收增长，积极推动Chiplet（芯粒）市场化应用,实现了规模性量产。',
    ]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Cross Encoder Reranking

• Dataset: train-eval
• Evaluated with <code>CERerankingEvaluator</code> with these parameters:

  {
      "at_k": 10
  }
  

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Dataset

Unnamed Dataset

• Size: 890 training samples
• Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
• Approximate statistics based on the first 890 samples:

  	sentence_0	sentence_1	label
  type	string	string	int
  details	<ul><li>min: 13 characters</li><li>mean: 55.08 characters</li><li>max: 237 characters</li></ul>	<ul><li>min: 64 characters</li><li>mean: 179.63 characters</li><li>max: 249 characters</li></ul>	<ul><li>0: ~80.00%</li><li>1: ~20.00%</li></ul>

• Samples:

  sentence_0	sentence_1	label
  <code>What is the significance of Samsung Electronics as a Korean brand in the list of the world's top 100 trademarks?</code>	<code>由于其正处于产品开发与验证投入阶段，影响了公司的投资收益。<br><br>　　设备企业：<br><br><br>　　业绩翻倍增长<br><br>　　虽然整体半导体板块尚未走出低谷，但国产替代需求推动下，设备环节企业保持逆周期高速增长，龙头设备厂商上半年业绩翻倍增长。国家统计局最新披露，围绕着克服“卡脖子”工程，今年上半年半导体相关行业制造业增长较快，半导体器件专用设备制造业增加值增长30.9%。</code>	<code>0</code>
  <code>根据文中提到的上游、中游和下游的不同环节，请简要描述半导体产业链的整体结构。</code>	<code>DRAM市场由三星、美光、海力士垄断了95%的份额，目前国产厂商合肥长鑫已经开始量产并在官网上架了相关产品，紫光集团也已建立DRAM事业部准备建厂。<br><br>NAND Flash的市场由三星、西数、铠侠等6家企业垄断。目前NAND Flash的发展方向是3D堆叠，国外先进企业均已纷纷开发出100层以上堆叠的NAND Flash。国产厂商长江存储已宣布128层产品研发成功，与国外先进企业的差距越来越小，已成为存储国产自主化的中坚力量。</code>	<code>0</code>
  <code>根据上下文信息，提出一个问题。</code>	<code>半导体材料是制作晶体管、集成电路、光电子器件的重要材料。<br><br>按照化学组成不同，半导体材料可以分为元素半导体和化合物半导体两大类。</code>	<code>0</code>

• Loss: <code>BinaryCrossEntropyLoss</code> with these parameters:

  {
      "activation_fn": "torch.nn.modules.linear.Identity",
      "pos_weight": null
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• num_train_epochs: 2
• fp16: True

All Hyperparameters

<details><summary>Click to expand</summary>

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 8
• 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: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 2
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• 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: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• 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}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• 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: {}

</details>

Training Logs

Framework Versions

• Python: 3.9.20
• Sentence Transformers: 5.0.0
• Transformers: 4.53.1
• PyTorch: 2.4.1
• Accelerate: 1.8.1
• Datasets: 3.6.0
• Tokenizers: 0.21.2

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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