Instructions to use felflare/bert-restore-punctuation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use felflare/bert-restore-punctuation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="felflare/bert-restore-punctuation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("felflare/bert-restore-punctuation") model = AutoModelForTokenClassification.from_pretrained("felflare/bert-restore-punctuation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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--- language: - en license: mit --- # β¨ bert-restore-punctuation []() This a bert-base-uncased model finetuned for punctuation restoration on [Yelp Reviews](https://www.tensorflow.org/datasets/catalog/yelp_polarity_reviews). The model predicts the punctuation and upper-casing of plain, lower-cased text. An example use case can be ASR output. Or other cases when text has lost punctuation. This model is intended for direct use as a punctuation restoration model for the general English language. Alternatively, you can use this for further fine-tuning on domain-specific texts for punctuation restoration tasks. Model restores the following punctuations -- [` ! ? . , - : ; '`] Model also restores upper-casing of words. ----------------------------------------------- ## π Usage Below is a quick way to get up and running with the model. 1. First, install the package. ```bash pip install rpunct ``` 2. β¦7a3eb4c
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