Instructions to use velvrix/RoBERTA_pipeiq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use velvrix/RoBERTA_pipeiq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="velvrix/RoBERTA_pipeiq")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("velvrix/RoBERTA_pipeiq") model = AutoModelForSequenceClassification.from_pretrained("velvrix/RoBERTA_pipeiq") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
roberta-base model is fine tuned on Kaggle two datasets namely "Sentiment Analysis Dataset" & Pre-processed Twitter tweets.
Respective labels and thier sentiment
- 0: 'NEUTRAL',
- 1: 'POSITIVE',
- 2: 'NEGATIVE'
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