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
| 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) | |