---
license: other
pipeline_tag: text-classification
widget:
- text: >-
     Sono quattro le persone indagate dalla Procura di Roma per le minacce via mail al ministro della Salute. Tra ottobre del 2020 e il gennaio del 2021 avrebbero inviato al ministro dei messaggi dal contenuto gravemente minaccioso. Al ministro la solidarietà di tutto il mondo politico e a causa della pandemia si assottigliano i redditi delle famiglie italiane. Aumenta anche la pressione fiscale. Lo rileva l'Istat.
- text: >-
    Le terapie intensive hanno superato la soglia del 30% di riempimento. La
    lotta al virus e anche lotta alle fake news, prosegue la collaborazione tra
    ministero della Salute e Twitter quando si cercano notizie sul Covid del
    Social rimanda le pagine del ministero, includendo anche le ultime
    informazioni sui vaccini. COVID-19 è stato l'hashtag più twittato a livello
    globale nel 2020. La poltrona negata da Erdogan ad Ursula von der Leyen, lo
    avete sentito? Fa ancora discutere dentro e fuori dal Parlamento europeo:
    Marco Clementi. 
- text: >-
    I bambini che soffrono di autismo hanno gli stessi diritti di tutti gli
    altri bambini sottolinea garante per l'infanzia, occorre dunque fare rete
    tra famiglia, scuola, pediatri e servizi sociali. Domani mattina alle 705 su
    Rai Uno torna la nostra rubrica di approfondimento 7 giorni. L'anticipazione
    nel servizio.
metrics:
- accuracy
- precision
- recall
language:
- it
---
# Model Card for raicrits/newsClassifier_v1

<!-- Provide a quick summary of what the model is/does. -->

This model analyses the input text and provides the class the text belongs to among the follofing ones:

0"sport"

1"giustizia-criminalita-sicurezza"

2"editoria-stampa-mass_media"

3"lavoro-previdenza"

4"trasporti"

5"cultura-scienze_umane"

6"esteri"

7"istruzione-formazione"

8"industria-impresa-produzione"

9"vita_e_cultura_religiosa"

10"sanita-salute"

11"economia-credito-finanza"

12"musica_e_spettacolo"

13"cronaca"

14"ambiente-natura-territorio"

15"politica-partiti-istituzioni-sindacati"

16"avvenimenti-celebrazioni-eventi_storici"

17"consumi-servizi"

18"individuo-famiglia-associazioni-societa"

19"commercio"

20"scienze-tecnologie"

21"pubblica_amministrazione-enti_locali"

22"tempo_libero"

23"arte-artigianato"

24"usi_e_costumi"

25"beni_culturali"

26"agricoltura-zootecnia"


## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Alberto Messina (alberto.messina@rai.it)
- **Model type:** BERT for Sequence Classification
- **Language(s) (NLP):** Italian
- **License:** TBD
- **Finetuned from model:** https://huggingface.co/xlm-roberta-base

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** N/A
- **Paper [optional]:** N/A
- **Demo [optional]:** N/A

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
The model should be used giving a short paragraph of text in Italian as input 
about which it is requested to get the most probable class.

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

TBA

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

The model should not be used as a general purpose classifier, i.e. on text which is not originated from news programme transcription or siilar content.


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The training dataset is made up of automatic transcriptions from RAI Italian newscasts, therefore there is an intrinsic bias in the kind
of topics included in the dataset.

## How to Get Started with the Model

Use the code below to get started with the model.

TBA

## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

TBA

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

TBA


#### Training Hyperparameters

- **Training regime:** Mixed Precision 

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->
TBA

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

TBA

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

TBA

### Results

TBA

#### Summary

TBA

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** 2 NVIDIA A100/40Gb
- **Hours used:** 2
- **Cloud Provider:** Private Infrastructure
- **Carbon Emitted:** 0.22 kg CO2 eq.

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

TBA

## More Information [optional]

TBA

## Model Card Authors [optional]

Alberto Messina

## Model Card Contact

alberto.messina@rai.it