Instructions to use DavinciTech/BERT_Categorizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DavinciTech/BERT_Categorizer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="DavinciTech/BERT_Categorizer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("DavinciTech/BERT_Categorizer") model = AutoModelForSequenceClassification.from_pretrained("DavinciTech/BERT_Categorizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signature# Model Name: DavinciTech/BERT_Categorizer ## Model Description This model is designed to categorize IT support tickets into ITIL-standard categories to streamline ticket routing and resolution in the IT support process. It's based on BERT, a transformer-based model that has been widely successful for a variety of NLP tasks. This model takes the text of an it support ticket as input in the format `"Title: <title text> Description: <body text>"`, and returns values for ITIL Impact, Urgency, and Type. ### How to Use The DavinciTech/BERT_Categorizer can be used directly after installation via the Hugging Face `transformers` library: ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer model_name = "DavinciTech/BERT_Categorizer" model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` # Example Usage ```python inputs = tokenizer("Example of a support ticket text", return_tensors="…8e50226 -
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