new slim example

examples/SLIM-Agents/using-slim-output-values.py

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+
+""" This example demonstrates the capabilities to use SLIM output values for programmatic evaluation, specifically
+    SLIM models that generate 'classification' oriented outputs.
+
+    One of the exciting features of the SLIM models is the ability to generate natural language directly, rather than
+    simply 'slotting' an answer into a predefined category - as a result, we believe that the SLIM models can
+    generalize better, as the model has the ability to explicitly draw upon the objective in generating a
+    response.
+
+    As a result, if you apply SLIM models to out-of-domain content, it is possible (even likely) that you may see a
+    different range of values than those outlined below
+
+    The following SLIM models have outputs that tend to be 'classifiers' or labelled 'categories' of specific values:
+
+    1.      sentiment   -   range of 3 values   -   positive, negative, neutral
+
+    2.      ratings     -   range of 5 values   -   1, 2, 3, 4, 5 ('degree' of sentiment)
+
+    3.      nli         -   range of 3 values   -   supports, contradicts, neutral
+
+    4.      emotions    -   ~35 emotion values  -   "afraid", "anger", "angry", "annoyed", "anticipating", "anxious",
+                                                    "apprehensive", "ashamed", "caring", "confident", "content",
+                                                    "devastated", "disappointed", "disgusted", "embarrassed",
+                                                    "excited", "faithful", "fear", "furious", "grateful", "guilty",
+                                                    "hopeful", "impressed", "jealous", "joy", "joyful", "lonely",
+                                                    "love", "nostalgic", "prepared", "proud", "sad", "sadness",
+                                                    "sentimental", "surprise", "surprised", "terrified", "trusting"
+
+    slim-category was trained on a diverse range of business, financial and general news documents with the goal of
+    defining the category or larger topic associated with a particular text
+
+    5.      category    -   ~27 category values -   "analyst", "announcements", "bonds", "business", "central bank",
+                                                    "commentary", "commodities", "currencies", "dividend", "earnings",
+                                                    "energy", "entertainment", "financials", "health",
+                                                    "human resources", "legal and regulation", "macroeconomics",
+                                                    "markets", "mergers and acquisitions", "opinion", "politics",
+                                                    "public markets", "science", "sports", "stocks", "tech", "world"
+
+    slim-intent was trained with wide range of materials from customer service and dialogs with a focus on trying to
+    classify the intent of the customer's request
+
+    6.      intent      -   ~14 values          -   "account", "cancel", "complaint", "customer service", "delivery",
+                                                    "feedback", "invoice", "new account", "order", "payments",
+                                                    "refund", "shipping", "subscription", "terminate"
+
+    7.      topics      -   Generative Topic    -   the topics model was trained primarily on complex financial and
+                                                    legal documents, but in our testing, the model generalizes very
+                                                    well to almost any text - and will be 'generative' in providing
+                                                    essentially a 1-2 word 'summary' of the text.
+
+    The following models are generally 'extractive' in that the output values will have a wide spectrum of potential
+    values, based on the subject text:
+    
+    8.  slim-extract
+
+    9.  slim-ner
+
+    10.  slim-tags
+
+    11.  slim-tags-3b
+
+    12.  slim-summary
+
+    13.  slim-boolean
+
+    14.  slim-xsum
+
+    15.  sli-sql
+
+    16.  slim-sentiment-ner
+
+    """
+
+
+from llmware.models import ModelCatalog
+
+models = ModelCatalog().list_function_call_models()
+
+for i, model_card in enumerate(models):
+
+    model_name = model_card["model_name"]
+
+    #   to view the "function call primary keys" for a selected model
+    keys = ModelCatalog().fc_primary_keys(model_name)
+
+    #   to view the expected range of output values
+    values = ModelCatalog().fc_output_values(model_name)
+
+    print("\nmodel_name: ", model_name)
+    print("primary keys/parameters: ", keys)
+    print("target output values: ", values)
+