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ft-bge-reranker-v2-m3-test

Text Ranking
sentence-transformers
Safetensors
xlm-roberta
cross-encoder
reranker
Generated from Trainer
dataset_size:890
loss:BinaryCrossEntropyLoss
Eval Results
Model card Files
xet
Community
ft-bge-reranker-v2-m3-test
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---
tags:
- sentence-transformers
- cross-encoder
- reranker
- generated_from_trainer
- dataset_size:890
- 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: train eval
      type: train-eval
    metrics:
    - type: map
      value: 0.9176616915422886
      name: Map
    - type: mrr@10
      value: 0.9176616915422886
      name: Mrr@10
    - type: ndcg@10
      value: 0.9377252954601817
      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) <!-- 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](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/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)

## 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("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>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CERerankingEvaluator) with these parameters:
  ```json
  {
      "at_k": 10
  }
  ```

| Metric      | Value      |
|:------------|:-----------|
| map         | 0.9177     |
| mrr@10      | 0.9177     |
| **ndcg@10** | **0.9377** |

<!--
## 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>](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

- `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
| Epoch  | Step | train-eval_ndcg@10 |
|:------:|:----:|:------------------:|
| 0.8929 | 100  | 0.9377             |


### 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
```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",
}
```

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