README.md

---
license: apache-2.0
language:
- it
pipeline_tag: text-classification
widget:
- text: "Ripartire la parola d'ordine, al governo chiediamo di accelerare la campagna sui vaccini e di lavorare a un cronoprogramma delle riaperture. Dobbiamo dare una prospettiva di rinascita a tutti gli italiani, dall'opposizione ancora all'attacco del governo, gli italiani sono esausti di fare sacrifici che non portano a nulla. 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."
---
# Model Card for raicrits/topicChangeDetector_v1

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

This model analyses the input text and provides an answer whether in the text there is a change of topic or not (resp. TOPPICCHANGE, SAMETOPIC).

## Model Details

### Model Description

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



- **Developed by:** Alberto Messina ([email protected])
- **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 an answer about whether or not it contains a change of topic.

### 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 topic change detector, 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 that can be tracked for change.

## 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

[email protected]