精调reranker(测试)

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,362 @@
+---
+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",
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,37 @@
+{
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "sentence_transformers": {
+    "activation_fn": "torch.nn.modules.activation.Sigmoid",
+    "version": "5.0.0"
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

eval/[email protected]

@@ -0,0 +1,3 @@
+epoch,steps,MAP,MRR@10,NDCG@10
+1.0,112,0.9110696517412935,0.9110696517412935,0.932689821546672
+2.0,224,0.9126865671641791,0.9126865671641791,0.9342285502703875

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 2271071852

special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
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+    "single_word": false
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+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
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+size 17083154

tokenizer_config.json

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+{
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+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 8192,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}