Instructions to use 4ldk/Roberta-Base-CoNLL2003 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 4ldk/Roberta-Base-CoNLL2003 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="4ldk/Roberta-Base-CoNLL2003")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("4ldk/Roberta-Base-CoNLL2003") model = AutoModelForTokenClassification.from_pretrained("4ldk/Roberta-Base-CoNLL2003") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -56,7 +56,6 @@ The following hyperparameters were used during training:
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- subword regularization p = 0.0 (= trained without subword regularization)
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And we add the sentences following the input sentence in the original dataset. Therefore, it cannot be reproduced from the dataset published on huggingface.
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For detail in [our github repository](https://github.com/4ldk/CoNLL2003_Choices/blob/develop/src/utils.py)).
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### Training results
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- subword regularization p = 0.0 (= trained without subword regularization)
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And we add the sentences following the input sentence in the original dataset. Therefore, it cannot be reproduced from the dataset published on huggingface.
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### Training results
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