Instructions to use twn39/bert-base-chinese-finetune-dianping with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use twn39/bert-base-chinese-finetune-dianping with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="twn39/bert-base-chinese-finetune-dianping")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("twn39/bert-base-chinese-finetune-dianping") model = AutoModelForSequenceClassification.from_pretrained("twn39/bert-base-chinese-finetune-dianping", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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library_name: transformers
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最终评估结果:
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library_name: transformers
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---
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最终评估结果:
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======================================================================
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eval_loss............................... 0.9778
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eval_accuracy........................... 0.7857
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eval_precision.......................... 0.7908
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eval_recall............................. 0.7857
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eval_f1................................. 0.7875
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eval_1star_f1........................... 0.8641
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eval_2star_f1........................... 0.7324
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eval_3star_f1........................... 0.6420
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eval_4star_f1........................... 0.8471
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eval_5star_f1........................... 0.7903
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eval_runtime............................ 32.9546
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eval_samples_per_second................. 273.0120
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eval_steps_per_second................... 4.2790
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epoch................................... 6.0000
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详细分类报告:
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======================================================================
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precision recall f1-score support
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1星 0.8581 0.8703 0.8641 1056
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2星 0.7533 0.7127 0.7324 1615
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3星 0.6075 0.6808 0.6420 1532
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4星 0.8688 0.8265 0.8471 3822
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5星 0.7627 0.8200 0.7903 972
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accuracy 0.7857 8997
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macro avg 0.7701 0.7821 0.7752 8997
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weighted avg 0.7908 0.7857 0.7875 8997
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