Instructions to use Heng666/codecarbon-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Heng666/codecarbon-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Heng666/codecarbon-text-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Heng666/codecarbon-text-classification") model = AutoModelForSequenceClassification.from_pretrained("Heng666/codecarbon-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: openrail
datasets:
- imdb
language:
- en
co2_eq_emissions:
emissions: 1.2207030395688
source: from AutoTrain, code carbon
training_type: fine-tuning
geographical_location: Singapore(SGP)
hardware_used: 1 x NVIDIA A100-SXM4-40GB