Instructions to use vppvgit/Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vppvgit/Finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vppvgit/Finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vppvgit/Finetuned") model = AutoModelForMaskedLM.from_pretrained("vppvgit/Finetuned") - Notebooks
- Google Colab
- Kaggle
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name: Masked Language Modeling
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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#
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This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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- name: BibliBERT
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name: Masked Language Modeling
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# BibliBERT
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This model is a fine-tuned version of [dbmdz/bert-base-italian-xxl-cased](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on the None dataset.
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It achieves the following results on the evaluation set:
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