Instructions to use terrytaylorbonn/final-opt-sentiment2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use terrytaylorbonn/final-opt-sentiment2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="terrytaylorbonn/final-opt-sentiment2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("terrytaylorbonn/final-opt-sentiment2") model = AutoModelForSequenceClassification.from_pretrained("terrytaylorbonn/final-opt-sentiment2") - Notebooks
- Google Colab
- Kaggle
OPT-125M Fine-Tuned for Sentiment Classification
This model is a fine-tuned version of facebook/opt-125m on the SST-2 dataset.
It performs binary sentiment classification: POSITIVE or NEGATIVE.
π Usage
You can use it with the π€ Transformers pipeline:
from transformers import pipeline
pipe = pipeline("text-classification", model="terrytaylorbonn/final-opt-sentiment2")
print(pipe("That movie was amazing!"))
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