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
File size: 1,099 Bytes
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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
<!-- Provide a longer summary of what this model is. -->
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](https://github.com/michelepapucci/llms-anatomy-course) course laboratory taught by [Michele Papucci](https://michelepapucci.github.io/) for the University of Pisa.
- **Developed by:** [Michele Papucci](https://michelepapucci.github.io/)
- **Model type:** BERT
- **Language(s) (NLP):** Italian
- **Finetuned from model [dbmdz/bert-base-italian-cased](dbmdz/bert-base-italian-cased):** Using [mteb/ItaCaseholdClassification](mteb/ItaCaseholdClassification)
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