Instructions to use serbanstein/bert_invoice_classfier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use serbanstein/bert_invoice_classfier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="serbanstein/bert_invoice_classfier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("serbanstein/bert_invoice_classfier") model = AutoModelForSequenceClassification.from_pretrained("serbanstein/bert_invoice_classfier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ab6b7b057204886f8a011e4dbdbd5f392ae06e1ee6aa4a88932d90341e892c8
- Size of remote file:
- 438 MB
- SHA256:
- 7a16a17a742e47696f60fe7ca247c6cbf156222b62781a40d05269403a7e6280
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