agentlans / mdeberta-v3-base-sentiment

huggingface.co
Total runs: 150
24-hour runs: -10
7-day runs: -304
30-day runs: -815
Model's Last Updated: October 12 2024
text-classification

Introduction of mdeberta-v3-base-sentiment

Model Details of mdeberta-v3-base-sentiment

Multilingual DeBERTa V3 Base for Sentiment Assessment

This is a fine-tuned version of the multilingual DeBERTa model (mdeberta) for assessing text sentiment across languages.

Model Details
  • Architecture: mdeberta-v3-base-sentiment
  • Task: Classification (Sentiment Analysis)
  • Training Data: agentlans/tatoeba-english-translations containing 48 900 labeled English translations
  • Input: Text in any of the supported languages by DeBERTa
  • Output: Sentiment score for text (positive, negative, neutral)
    • positive scores indicate a positive sentiment
    • zero score indicate neutral sentiment
    • negative scores indicate a negative sentiment
Performance

RMSE accuracy on 20% held-out validation set: 0.4177

Training Data

The model was trained on agentlans/tatoeba-english-translations .

Usage
  • Preprocess text to remove special characters, convert all letters to lowercase
Limitations
  • Performance may vary for texts significantly different from the training data
  • Output is based on statistical patterns and may not always align with human judgment
  • Sentiment is assessed purely on textual features, not considering factors like subject familiarity or cultural context
Ethical Considerations
  • Should not be used as the sole determinant of text suitability for specific audiences
  • Results may reflect biases present in the training data sources
  • Care should be taken when using these models in educational or publishing contexts

Runs of agentlans mdeberta-v3-base-sentiment on huggingface.co

150
Total runs
-10
24-hour runs
-21
3-day runs
-304
7-day runs
-815
30-day runs

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mdeberta-v3-base-sentiment install

mdeberta-v3-base-sentiment is an open source model from GitHub that offers a free installation service, and any user can find mdeberta-v3-base-sentiment on GitHub to install. At the same time, huggingface.co provides the effect of mdeberta-v3-base-sentiment install, users can directly use mdeberta-v3-base-sentiment installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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