![msm download tool param preprocessing msm download tool param preprocessing](https://ihfoam.ihcantabria.com/wp-content/uploads/2019/10/preProcessing-02.png)
Haystack is also compatible with the latest Transformers v4.20.1 release and we will continuously ensure that you can benefit from the latest features in Haystack! Other Changes Pipeline PyTorch shared an impressive analysis of speedups over CPU-only here. Haystack is now compatible with last week's PyTorch v1.12 release so that you can take advantage of Apple silicon GPUs (Apple M1) for accelerated training and evaluation. retrieve( query = "In which house is Harry Potter?"))īig thanks to our community member for the PR! Torch 1.12 and Transformers 4.20.1 Support # Translate a text query to a SPARQL query and execute it on the knowledge graph print( kgqa_retriever. # Initialize retriever from pre-trained model kgqa_retriever = Text2SparqlRetriever( knowledge_graph = kg, model_name_or_path = Path( "./saved_models/tutorial10/hp_v3.4")) import_from_ttl_file( index = "tutorial10", path = Path( "data/tutorial10/triples.ttl"))
![msm download tool param preprocessing msm download tool param preprocessing](https://global-uploads.webflow.com/5e5bab7863723e1de6a7f5f1/5f16a015cf7d114bbc374f64_cloud-left.png)
![msm download tool param preprocessing msm download tool param preprocessing](https://ihfoam.ihcantabria.com/wp-content/uploads/2019/10/preProcessing-01.png)
# Initialize knowledge graph and import triples from a ttl file kg = InMemoryKnowledgeGraph( index = "tutorial10") # Fetch a pre-trained BART model that translates text queries to SPARQL queries fetch_archive_from_http( url = "", output_dir = "./saved_models/tutorial10/") utils import fetch_archive_from_http # Fetch data represented as triples of subject, predicate, and object statements fetch_archive_from_http( url = "", output_dir = "data/tutorial10") document_stores import InMemoryKnowledgeGraph from haystack. nodes import Text2SparqlRetriever from haystack.