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Fusion 5.12
    Fusion 5.12

    Train a Smart Answers Supervised Model

    The Supervised solution for Smart Answers begins with training a model using your existing data and the Smart Answers Supervised Training job (QnA Supervised Training job in 5.1 and 5.2), as explained in this topic. The job includes an auto-tune feature that you can use instead of manually tuning the configuration.

    Training job requirements


    150GB plus 2.5 times the total input data size.

    Processor and memory

    The memory requirements depend on whether you choose GPU or CPU processing:

    • one core

    • 11GB RAM

    • 32 cores

    • 32GB RAM

    If your training data contains more than 1 million entries, use GPU.

    1. Prepare the input data

    1. Format your input data as question/answer pairs, that is, a query and its corresponding response in each row.

      You can do this in any format that Fusion supports.

      If there are multiple possible answers for a unique question, then repeat the questions and put the pair into different rows to make sure each row has one question and one answer, as in the example JSON below:

      [{"question":"How to transfer personal auto lease to business auto lease?","answer":"I would approach the lender that you are getting the lease from..."}
       {"question":"How to transfer personal auto lease to business auto lease?","answer":"See what the contract says about transfers or subleases..."}]
    2. Index the input data in Fusion.

      If you wish to have the training data in Fusion, index it into a separate collection for training data such as model_training_input. Otherwise you can use it directly from the cloud storage.

    2. Configure the training job

    1. In Fusion, navigate to Collections > Jobs.

    2. Select Add > Smart Answers Supervised Training:

      Select the Smart Answers Supervised Training job

    3. In the Training Collection field, specify the input data collection that you created when you prepared the input data.

      In Fusion 5.3 and later, you can also configure this job to read from or write to cloud storage. See Configure An Argo-Based Job to Access GCS and Configure An Argo-Based Job to Access S3.
    4. Enter the names of the Question Field and the Answer Field in the training collection.

    5. Enter a Model Deployment Name.

      The new machine learning model will be saved in the blob store with this name. You will reference it later when you configure your pipelines.

    6. Fusion 5.3 and later: Configure the Model base.

      There are several pre-trained word and BPE embeddings for different languages, as well as a few pre-trained BERT models.

      If you want to train custom embeddings, select word_custom or bpe_custom. This trains Word2vec on the provided data and specified fields. It might be useful in cases when your content includes unusual or domain-specific vocabulary.

      If you have content in addition to the query/response pairs that can be used to train the model, then specify it in the Texts Data Path.

      When you use the pre-trained embeddings, the log shows the percentage of processed vocabulary words. If this value is high, then try using custom embeddings.

      The job trains a few (configurable) RNN layers on top of word embeddings or fine-tunes a BERT model on the provided training data. The result model uses an attention mechanism to average word embeddings to obtain the final single dense vector for the content.

      Dimension size of vectors for Transformer-based models is 768. For RNN-based models it is 2 times the number units of the last layer. To find the dimension size: download the model, expand the zip, open the log and search for Encoder output dim size: line. You might need this information when creating collections in Milvus.
    7. Optional: Check Perform auto hyperparameter tuning to use auto-tune.

      Although training module tries to select the most optimal default parameters based on the training data statistics, auto-tune can extend it by automatically finding even better training configuration through hyper-parameter search. Although this is a resource-intensive operation, it can be useful to identify the best possible RNN-based configuration. Transformer-based models like BERT are not used during auto hyperparameter tuning as they usually perform better yet they are much more expensive on both training and inference time.

    8. Click Save.

      The saved job configuration

      If using solr as the training data source ensure that the source collection contains the random_* dynamic field defined in its managed-schema. This field is required for sampling the data. If it is not present, add the following entry to the managed-schema alongside other dynamic fields <dynamicField name="random_*" type="random"/> and <fieldType class="solr.RandomSortField" indexed="true" name="random"/> alongside other field types.
    9. Click Run > Start.

    After training is finished the model is deployed into the cluster and can be used in index and query pipelines.