Join ๐๐ Text Classification# Monitoring Inference Predictions FastAPI MLOps Steps: Deploying, Monitoring NLP Tasks: TextClassification, TokenClassification (NER) Libraries: spaCy, FastAPI, transformers ๐ Using modAL for Active Learning MLOps Steps: Training NLP Tasks: TextClassification Libraries: modAL Techniques: Active Learning ๐ฐ Building a news classifier with weak supervision MLOps Steps: Labelling NLP Tasks: TextClassification (news) Libraries: Argilla, snorkel, sklearn Techniques: Weak Supervision ๐ Weak supervision in multi-label text classification tasks MLOps Steps: Labelling, Training NLP Tasks: TextClassification (multi-label) Libraries: Argilla, scikit-multilearn Techniques: Weak Supervision ๐ง Find label errors with cleanlab MLOps Steps: Training, Monitoring NLP Tasks: TextClassification Libraries: cleanlab Techniques: explainability ๐ต๏ธโโ๏ธ Analyzing predictions with model explainability methods MLOps Steps: Monitoring NLP Tasks: TextClassification Libraries: shap, transformers-interpret Techniques: Explainability ๐งผ Clean labels using your model loss MLOps Steps: Monitoring NLP Tasks: TextClassification Libraries: Argilla, transformers Techniques: Explainability ๐งฑ Extending weak supervision workflows with sentence embeddings MLOps Steps: Labelling, Training NLP Tasks: TextClassification Libraries: Argilla, sentence-transformers Techniques: Weak Supervision ๐คฏ Few-shot classification with SetFit and a custom dataset MLOps Steps: Training NLP Tasks: TextClassification Libraries: setfit Techniques: few-shot ๐ Active learning for text classification with small-text MLOps Steps: Training NLP Tasks: TextClassification Libraries: small-text Techniques: Active Learning ๐ท๏ธ Label your data to fine-tune a classifier with Hugging Face MLOps Steps: Labelling, Training NLP Tasks: TextClassification (sentiment) Libraries: transformers Techniques: basics