How to use lochhonest/finetuned_mbert with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lochhonest/finetuned_mbert") sentences = [ "What is the purpose of the analysis steps outlined in the document?", "Structure of the document\n\nThe structure of the present document is as follows:\n\n- Chapter [sasguide:par:analysis] introduces the investigator to the\n analysis of XMM-Newton\n (http://www.cosmos.esa.int/web/xmm-newton/technical-details) data.\n It provides a brief description of XMM-Newton\n (http://www.cosmos.esa.int/web/xmm-newton/technical-details)\n observation and calibration files and outlines the analysis steps\n required to produce calibrated event files and to extract scientific\n products.\n\n- Chapter [sasguide:par:gui] describes the SAS graphical user\n interface (GUI), a user friendly tool which enables SAS interactive\n analysis tasks to be run without using the command line.\n\n- Chapters [sasguide:par:epic], [sasguide:par:rgs] and\n [sasguide:par:om] describe the SAS analysis steps required to obtain\n EPIC\n (http://www.cosmos.esa.int/web/xmm-newton/technical-details-epic),\n RGS (http://www.cosmos.esa.int/web/xmm-newton/technical-details-rgs)\n and OM\n (http://www.cosmos.esa.int/web/xmm-newton/technical-details-om) data\n products, respectively, which can be used afterwards by standard\n astronomical software packages.\n\n- Chapter [sasguide:xmmextractor] gives an overview of a SAS procedure\n to produce high-level science products for XMM-Newton\n (http://www.cosmos.esa.int/web/xmm-newton/technical-details) cameras\n from the raw, uncalibrated, data files contained in the Observation\n Data File (ODF). The procedure allows for some interactivity which\n lets the user take decisions concerning the analysis process.\n", "ommosaic\n- Whilst tests have so far shown that scattered-light features do not\n effect the cross-correlation algorithm, further testing is underway\n and the method should be used with caution.\n\n- Tests using the cross-algorithm on aspect-corrected sky-images have\n so-far shown the computed offsets to be small (less than 0.2\n pixels). If both the aspect-correction and the cross-correlation\n algorithm were perfect, they should be zero. However the error in\n the aspect-correction can be up to 1 arc sec, and further testing\n needs to be done to evaluate any differences.\n\n- It would be desireable to perform a further aspect correction on the\n sky-images- this would require either a new OM task or, possibly, a\n modification to omsrclistcomb. Note that even if the sky-images have\n not been aspect-corrected, the coordinates of the sources in the\n observation source-list file may have.\n", "emchain\nThe chain will adapt to the evolution of its constituents and to the\norganisation of the pipeline.\n\nThe current implementation is a Perl script.\n" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
How to fix it?
