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. -->

Model Sources [optional]

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

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 as input a short paragraph of text taken from a news programme or article in Italian about which it is requested to get an answer about whether or not it contains a change of topic. The model has been trained to detect topic changes without apriori knowledge of possible points of separation (e.g., paragraphs or speaker turns). For this reason it tends to be sensitive to the amount of text supposed to belong to either of the two subsequent topics, and therefore performs better when the sought for topic change occurs approximately in the middle of the input. To reduce the impact of this issue, it is suggested to use the model on a sequence of partially overlapping pieces of text taken from the document to be analysed, and to further process the results sequence to consolidate a decision.

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

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 presented in Lacoste et al. (2019).

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]

The development of this model is partially supported by H2020 Project AI4Media - A European Excellence Centre for Media, Society and Democracy (Grant nr. 951911) - http://ai4media.eu

Model Card Authors [optional]

Alberto Messina

Model Card Contact

alberto.messina@rai.it