Join 🔎 Explainability and bias# These tutorials show you how to explain you data using Argilla. 🗺️ Adding bias-equality features to text with disaggregators MLOps Steps: Labelling NLP Tasks: Text2Text Libraries: Disaggregators Techniques: Explainability 🔐 Backup and version Argilla Datasets using DVC MLOps Steps: Deploying, Monitoring NLP Tasks: Text2Text Libraries: dvc Techniques: 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 💡 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 🧼 Clean labels using your model loss MLOps Steps: Monitoring NLP Tasks: TextClassification Libraries: Argilla, transformers Techniques: Explainability 💫 Explore and analyze spaCy NER pipelines MLOps Steps: Labelling NLP Tasks: TokenClassification (NER) Libraries: spaCy Techniques: Explainability 💡 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
