Instructions to use vikaskapur/sentimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikaskapur/sentimental with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vikaskapur/sentimental")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vikaskapur/sentimental") model = AutoModelForSequenceClassification.from_pretrained("vikaskapur/sentimental") - Notebooks
- Google Colab
- Kaggle
Model Details
- The SENTIMENTAL classifier trained to predict the likelihood that a comment will be perceived as positive or negative.
- BERT based Text Classification.
Intended Use
- Intended to be used for a wide range of use cases such as supporting human moderation and extracting polarity of review comments.
- Not intended for fully automated moderation.
- Not intended to make judgments about specific individuals.
Factors
- Identity terms referencing frequently positive and negative emotions.
Metrics
• Accuracy, which measures the percentage of True Positive and True Negative.
Ethical Considerations
- TODO
Quantitative Analyses
- TODO
Training Data
- TODO
Evaluation Data
- TODO
Caveats and Recommendations
- TODO
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