Instructions to use imagine0711/bert-base-chinese-finetuned-tcfd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use imagine0711/bert-base-chinese-finetuned-tcfd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="imagine0711/bert-base-chinese-finetuned-tcfd")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("imagine0711/bert-base-chinese-finetuned-tcfd") model = AutoModelForMaskedLM.from_pretrained("imagine0711/bert-base-chinese-finetuned-tcfd") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): d87de69
Training in progress epoch 4
Browse files- README.md +6 -5
- tf_model.h5 +1 -1
README.md
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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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- Train Loss: 0.
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- Train Accuracy: 0.
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- Validation Loss: 0.
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- Validation Accuracy: 0.
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- Epoch:
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## Model description
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| 0.8356 | 0.0571 | 0.7513 | 0.0585 | 1 |
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| 0.7771 | 0.0584 | 0.7556 | 0.0602 | 2 |
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| 0.6974 | 0.0590 | 0.6988 | 0.0589 | 3 |
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### Framework versions
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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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- Train Loss: 0.6641
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- Train Accuracy: 0.0599
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- Validation Loss: 0.5843
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- Validation Accuracy: 0.0609
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- Epoch: 4
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## Model description
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| 0.8356 | 0.0571 | 0.7513 | 0.0585 | 1 |
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| 0.7771 | 0.0584 | 0.7556 | 0.0602 | 2 |
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| 0.6974 | 0.0590 | 0.6988 | 0.0589 | 3 |
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| 0.6641 | 0.0599 | 0.5843 | 0.0609 | 4 |
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### Framework versions
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tf_model.h5
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size 475933304
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version https://git-lfs.github.com/spec/v1
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size 475933304
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