# SklearnTFIDFEmbedder **`class SklearnTFIDFEmbedder(config)`** Bases: [`Embedder`](squirro.lib.nlp.steps.embedders.Embedder.md#squirro.lib.nlp.steps.embedders.Embedder) The TFIDF [`Embedder`](squirro.lib.nlp.steps.embedders.Embedder.md#squirro.lib.nlp.steps.embedders.Embedder) encodes provided text based on the sklearn [TFIDF-Vectorizer](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html). **Input** - the input field needs to be of type [`str`](https://docs.python.org/3.11/library/stdtypes.html#str). **Output** - the output field is filled with data of type `numpy.ndarray` Parameters - `type` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)) – sklearn_tfidf - `model_kwargs` ([`dict`](https://docs.python.org/3.11/library/stdtypes.html#dict)`, ``{}`) – Keyword arguments to pass on to sklearn TfidfVectorizer **Example** ```json { "step": "embedder", "type": "sklearn_tfidf", "name": "sklearn_tfidf", "input_field": "body", "model_kwargs": { "min_df": 5, "ngram_range": "1, 3" }, "output_field": "embedded_body" } ``` Methods SummaryMethods Documentation **`load()`** Load a step **`process_batch(batch)`** Process a batch of documents. If not defined will default to using self.process_doc for each document in the batch. Parameters `batch` ([`list`](https://docs.python.org/3.11/library/stdtypes.html#list)`(`[`Document`](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)`)`) – List of documents Returns List of processed documents Return type [list](https://docs.python.org/3.11/library/stdtypes.html#list)([Document](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)) **`save()`** Save a step **`train(docs)`** Train on a step of a set of documents Parameters `docs` (`generator``(`[`Document`](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)`)`) – Generator of documents Returns Generator of processed documents Return type generator([Document](../technical/libnlp/base.md#squirro.lib.nlp.document.Document))