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lzun/mcovmex-text

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face
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Model Details

Model Description

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  • —Developed by: Luis Zúñiga
  • —Model type: Text Classification
  • —Language(s) (NLP): Spanish
  • —License: Apache 2.0
  • —Finetuned from model [optional]: dccuchile/bert-base-spanish-wwm-cased

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Uses

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The main purpose of this model is to serve as a text representation tool for text classification of social media publications (mainly tweets) related to COVID-19 in Spanish.

However, this only the first part of a multimodal model, where the image model is used to represent images and then, with a fusion method, text and images can be combined for a better representation of publications containing text and images.

Direct Use

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This model can be used to directly assess the sentiment polarity of text elements of social media publications. However, the main usage is to be combined with the image model for multimodal sentiment analysis.

Bias, Risks, and Limitations

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The dataset used to train this model contains tweets related to COVID-19 and many other subjects (like sports, politics, economics, to name a few), so it does not specializes in a particular topic.

How to Get Started with the Model

Use the code below to get started with the model.

py
model_ckpt = 'dccuchile/bert-base-spanish-wwm-uncased'
tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
text_feature_extractor = pipeline(task = 'text-classification', model = 'lzun/mcovmex-text', tokenizer = tokenizer)

Training Details

Training Data

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The model is trained with the MCOVMEX dataset, specifically the text from tweets. The classes are positive (1), negative (-1), neutral (0) and spam (2). However, spam and neutral classes can be combined together to form a three class classification problem. The training data is not available due to its sensitive content, but can be shared upon reasonable request.

Evaluation

Metrics

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As recommended, we use Matthew's Correlation Coefficient as the main evaluation metric due to the data imbalanceness. However, we keep track of accuracy, balanced accuracy and weighted F1.

Results

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Summary

Citation [optional]

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