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 ·
4a93c16
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Parent(s): c2efc5e
Training in progress epoch 7
Browse files
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: 0.0605
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- Epoch:
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## Model description
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| 0.6641 | 0.0599 | 0.5843 | 0.0609 | 4 |
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| 0.6423 | 0.0599 | 0.6116 | 0.0605 | 5 |
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| 0.6540 | 0.0596 | 0.6470 | 0.0605 | 6 |
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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: 0.6361
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- Train Accuracy: 0.0595
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- Validation Loss: 0.6676
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- Validation Accuracy: 0.0605
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- Epoch: 7
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## Model description
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| 0.6641 | 0.0599 | 0.5843 | 0.0609 | 4 |
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| 0.6423 | 0.0599 | 0.6116 | 0.0605 | 5 |
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| 0.6540 | 0.0596 | 0.6470 | 0.0605 | 6 |
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| 0.6361 | 0.0595 | 0.6676 | 0.0605 | 7 |
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### Framework versions
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