--- license: apache-2.0 tags: - text-classification - generic library_name: generic --- ## Hugging Face Transformers with Scikit-learn Classifiers 🤩🌟 This repository contains a small proof-of-concept pipeline that leverages longformer embeddings with scikit-learn Logistic Regression that does sentiment analysis. The training leverages the language module of [whatlies](https://github.com/koaning/whatlies). # Classification Report Below is the classification report 👇🏻 precision recall f1-score support 0 0.84 0.89 0.86 53 1 0.86 0.81 0.84 47 accuracy 0.85 100 macro avg 0.85 0.85 0.85 100 weighted avg 0.85 0.85 0.85 100 # Pipeline Below you can see the pipeline 👇🏻 (it's interactive! 🪄)

Pipeline(steps=[('embedding',\n                 HFTransformersLanguage(model_name_or_path='allenai/longformer-base-4096')),\n                ('model', LogisticRegression())])

Please rerun this cell to show the HTML repr or trust the notebook.

# Hyperparameters ``` -'memory': None, -'steps': [('embedding', HFTransformersLanguage(model_name_or_path='allenai/longformer-base-4096')), ('model', LogisticRegression())], - 'verbose': False, -'embedding': HFTransformersLanguage(model_name_or_path='allenai/longformer-base-4096'), -'model': LogisticRegression(), -'embedding_model_name_or_path': 'allenai/longformer-base-4096', -'model_C': 1.0, - 'model_class_weight': None, - 'model_dual': False, - 'model_fit_intercept': True, - 'model_intercept_scaling': 1, - 'model_l1_ratio': None, - 'model_max_iter': 100, - 'model_multi_class': 'auto', -'model_n_jobs': None, -'model_penalty': 'l2', -'model_random_state': None, -'model_solver': 'lbfgs', -'model_tol': 0.0001, -'model_verbose': 0, -'model_warm_start': False ```