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rag-qa-base-bert

Question Answering
Transformers
Safetensors
Chinese
bert
Model card Files
xet
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rag-qa-base-bert
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---
license: apache-2.0
language:
- zh
pipeline_tag: question-answering
library_name: transformers
---
---

library_name: transformers

license: apache-2.0

base_model: bert-base-chinese

tags:

- question-answering

- generated_from_trainer

metrics: '{"exact": 58.711182388103225, "f1": 58.7488457987073, "total": 6859, "HasAns_exact":

  34.578402366863905, "HasAns_f1": 34.67393984220908, "HasAns_total": 2704, "NoAns_exact":

  74.41636582430806, "NoAns_f1": 74.41636582430806, "NoAns_total": 4155, "best_exact":

  63.58069689459105, "best_exact_thresh": 8.853434701450169e-05, "best_f1": 63.59284638188268,

  "best_f1_thresh": 8.853434701450169e-05}'

model-index:

- name: rag-qa-base-bert

  results: []

---



<!-- This model card has been generated automatically according to the information the Trainer had access to. You

should probably proofread and complete it, then remove this comment. -->



# rag-qa-base-bert



This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.



## Model description



More information needed



## Intended uses & limitations



More information needed



## Training and evaluation data



More information needed



## Training procedure



### Training hyperparameters



The following hyperparameters were used during training:

- learning_rate: 2e-05

- train_batch_size: 8

- eval_batch_size: 8

- seed: 42

- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments

- lr_scheduler_type: linear

- num_epochs: 3

- mixed_precision_training: Native AMP



### Training results







### Framework versions



- Transformers 4.57.3

- Pytorch 2.11.0+cu128

- Datasets 5.0.0

- Tokenizers 0.22.2