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--- |
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license: apache-2.0 |
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language: |
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- it |
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pipeline_tag: text-classification |
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widget: |
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- 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." |
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- 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. " |
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- 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." |
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--- |
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# Model Card for raicrits/topicChangeDetector_v1 |
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<!-- Provide a quick summary of what the model is/does. --> |
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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). |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Alberto Messina ([email protected]) |
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- **Model type:** BERT for Sequence Classification |
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- **Language(s) (NLP):** Italian |
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- **License:** TBD |
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- **Finetuned from model:** https://huggingface.co/xlm-roberta-base |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** N/A |
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- **Paper [optional]:** N/A |
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- **Demo [optional]:** N/A |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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The model should be used giving a short paragraph of text in Italian as input |
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about which it is requested to get an answer about whether or not it contains a change of topic. |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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TBA |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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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. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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The training dataset is made up of automatic transcriptions from RAI Italian newscasts, therefore there is an intrinsic bias in the kind |
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of topics that can be tracked for change. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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TBA |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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TBA |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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TBA |
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#### Training Hyperparameters |
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- **Training regime:** Mixed Precision |
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## Evaluation |
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TBA |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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TBA |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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TBA |
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### Results |
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TBA |
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#### Summary |
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TBA |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** 2 NVIDIA A100/40Gb |
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- **Hours used:** 2 |
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- **Cloud Provider:** Private Infrastructure |
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- **Carbon Emitted:** 0.22 kg CO2 eq. |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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TBA |
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## More Information [optional] |
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TBA |
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## Model Card Authors [optional] |
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Alberto Messina |
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## Model Card Contact |
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[email protected] |
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