newsClassifier_v2

Model Card for raicrits/newsClassifier_v2

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

"0": "MEDICINA-E-SALUTE", "1": "AMBIENTE-E-TERRITORIO", "2": "CRIMINI-E-REATI", "3": "RELIGIONE", "4": "TECNOLOGIA-E-SCIENZA", "5": "DIRITTI-UMANI", "6": "ARTE-E-PATRIMONIO-CULTURALE", "7": "AGRICOLTURA-E-ALIMENTAZIONE", "8": "SPORT", "9": "DIRITTO-E-GIUSTIZIA", "10": "ISTRUZIONE-E-RICERCA", "11": "POLITICA-INTERNA", "12": "EMERGENZE-E-SOCCORSO", "13": "SPETTACOLO-E-INTRATTENIEMNTO", "14": "ECONOMIA-E-FINANZA", "15": "FISCO-E-POLITICHE-ECONOMICHE", "16": "TEMPO-LIBERO-E-TURISMO", "17": "COMUNICAZIONE-E-MEDIA", "18": "FAMIGLIA-E-RELAZIONI", "19": "SICUREZZA-E-DIFESA", "20": "CITTA-E-URBANISTICA", "21": "SOCIETA-E-FENOMENI-SOCIALI", "22": "LAVORO-E-IMPRESA", "23": "POLITICA-ESTERA", "24": "TRASPORTI-E-INFRASTRUTTURE", "25": "STORIA-E-MEMORIA", "26": "SESSUALITA", "27": "POLITICA"

Model Details

Model Description

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• 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]

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• Repository: N/A
• Paper [optional]: N/A
• Demo [optional]: N/A

Uses

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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

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TBA

Out-of-Scope Use

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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

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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 of topics included in the dataset.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

• Training regime: Mixed Precision

Evaluation

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TBA

Testing Data, Factors & Metrics

Testing Data

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TBA

Metrics

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Results

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: 2 NVIDIA H100
• Hours used: 0.5
• Cloud Provider: Private Infrastructure
• Carbon Emitted: 0.17 kg CO2 eq.

Glossary [optional]

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TBA

More Information [optional]

TBA

Model Card Authors [optional]

Alberto Messina

Model Card Contact

[email protected]