Instructions to use mjavadmt/simple_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjavadmt/simple_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mjavadmt/simple_classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mjavadmt/simple_classification") model = AutoModelForSequenceClassification.from_pretrained("mjavadmt/simple_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 167 Bytes
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datasets:
- GEM/xwikis
metrics:
- bleu
widget:
- text: I'm going to stay here
- text: this is fantastic
- text: what a bad sh
pipeline_tag: text-classification
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