Instructions to use tdobrxl/Sentiment_analysis_PI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tdobrxl/Sentiment_analysis_PI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tdobrxl/Sentiment_analysis_PI")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tdobrxl/Sentiment_analysis_PI") model = AutoModelForSequenceClassification.from_pretrained("tdobrxl/Sentiment_analysis_PI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tdobrxl/Sentiment_analysis_PI")
model = AutoModelForSequenceClassification.from_pretrained("tdobrxl/Sentiment_analysis_PI", device_map="auto")Quick Links
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tdobrxl/Sentiment_analysis_PI")