Instructions to use mpapucci/BertForItaCaseholdClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mpapucci/BertForItaCaseholdClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mpapucci/BertForItaCaseholdClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mpapucci/BertForItaCaseholdClassification") model = AutoModelForSequenceClassification.from_pretrained("mpapucci/BertForItaCaseholdClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: transformers
tags:
- administrative
- public_administration
datasets:
- mteb/ItaCaseholdClassification
language:
- it
metrics:
- f1
base_model:
- dbmdz/bert-base-italian-cased
pipeline_tag: text-classification
Model Card for Model ID
The model is a Bert fine-tuned to do Text Classification on relevant categories for the Italian Public administration.
Model Details
Model Description
The model is a Bert fine-tuned to do Text Classification on relevant categories for the Italian Public administration. This has been done for the LLMs Anatomy course laboratory taught by Michele Papucci for the University of Pisa.
- Developed by: Michele Papucci
- Model type: BERT
- Language(s) (NLP): Italian
- Finetuned from model dbmdz/bert-base-italian-cased: Using mteb/ItaCaseholdClassification