# TermsExtractionEmbedder **`class TermsExtractionEmbedder(config)`** Bases: [`Embedder`](squirro.lib.nlp.steps.embedders.Embedder.md#squirro.lib.nlp.steps.embedders.Embedder) The term extraction [`Embedder`](squirro.lib.nlp.steps.embedders.Embedder.md#squirro.lib.nlp.steps.embedders.Embedder) allows to extract significant terms provided textual data. **Note** - the significant terms get extracted based on tf/idf. There is some text cleaning, html removal, stopword removal, noun chunking, lemmatization etc. done prior to the extraction. **Input** - all input fields need 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 [`list`](https://docs.python.org/3.11/library/stdtypes.html#list) [ [`str`](https://docs.python.org/3.11/library/stdtypes.html#str) ]. Parameters - `type` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)) – terms_extraction - `input_field` ([`list`](https://docs.python.org/3.11/library/stdtypes.html#list)`, ``[``]`) – list of fields to embed from - `filter_list` ([`list`](https://docs.python.org/3.11/library/stdtypes.html#list)`, ``[``]`) – extra words to added to the stopword list - `max_content_length` ([`int`](https://docs.python.org/3.11/library/functions.html#int)`, ``50000`) – maximum document length to be processed - longer documents are cut off - `min_word_len` ([`int`](https://docs.python.org/3.11/library/functions.html#int)) – minimal word length - `max_chunk_len` ([`int`](https://docs.python.org/3.11/library/functions.html#int)) – max chunk length - `max_cores` ([`int`](https://docs.python.org/3.11/library/functions.html#int)`, ``-1`) – max number of cpu cores used - `save_model` ([`bool`](https://docs.python.org/3.11/library/functions.html#bool)) – save the tf-idf model - `cache_lemmas` ([`bool`](https://docs.python.org/3.11/library/functions.html#bool)) – cache the lemmatized words - `p_significant_terms` ([`float`](https://docs.python.org/3.11/library/functions.html#float)) – % of significant term to be extracted - `add_lemmization` ([`bool`](https://docs.python.org/3.11/library/functions.html#bool)) – lemmatize input text **Example** ```json { "step": "embedder", "type": "terms_extraction", "input_field": ["text"], "output_field": "significant_terms", "max_chunk_len": 2, "min_word_len": 4, "p_significant_terms": 0.2, "add_lemmization": false, "cache_lemmas": false, "save_model": false } ``` Methods SummaryMethods Documentation **`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))