Join 📊 Monitoring# These tutorials show you how Argilla can help you to monitor your model predictions. Monitoring Inference Predictions FastAPI MLOps Steps: Deploying, Monitoring NLP Tasks: TextClassification, TokenClassification (NER) Libraries: spaCy, FastAPI, transformers 💡 Building and testing a zero-shot sentiment classifier with GPT-3 and Argilla MLOps Steps: Labelling NLP Tasks: TextClassification Libraries: OpenAI Techniques: Few-shot, Explainability 🧐 Find label errors with cleanlab MLOps Steps: Training, Monitoring NLP Tasks: TextClassification Libraries: cleanlab Techniques: explainability 🕵️♀️ Analyzing predictions with model explainability methods MLOps Steps: Monitoring NLP Tasks: TextClassification Libraries: shap, transformers-interpret Techniques: Explainability 🧼 Clean labels using your model loss MLOps Steps: Monitoring NLP Tasks: TextClassification Libraries: Argilla, transformers Techniques: Explainability 🥇 Compare Text Classification Models MLOps Steps: Monitoring NLP Tasks: TextClassification Libraries: Argilla, SetFit Techniques: Zero-shot classification
