Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use wsqstar/bert-finetuned-weibo-luobokuaipao with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wsqstar/bert-finetuned-weibo-luobokuaipao with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wsqstar/bert-finetuned-weibo-luobokuaipao")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wsqstar/bert-finetuned-weibo-luobokuaipao") model = AutoModelForSequenceClassification.from_pretrained("wsqstar/bert-finetuned-weibo-luobokuaipao") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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base_model: bert-base-chinese
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tags:
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- generated_from_trainer
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model-index:
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- name: bert-finetuned-weibo-luobokuaipao
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results: []
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# bert-finetuned-weibo-luobokuaipao
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This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
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## Model description
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### Training results
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### Framework versions
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base_model: bert-base-chinese
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: bert-finetuned-weibo-luobokuaipao
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results: []
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# bert-finetuned-weibo-luobokuaipao
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This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1877
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- Accuracy: 0.6322
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## Model description
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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| No log | 1.0 | 198 | 1.0607 | 0.5995 |
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| No log | 2.0 | 396 | 0.9555 | 0.6448 |
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| 0.6334 | 3.0 | 594 | 1.1877 | 0.6322 |
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### Framework versions
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