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Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
With python3.10, the code fails with `ImportError: cannot import name 'soft_unicode' from 'markupsafe'`. https://github.com/aws/aws-sam-cli/issues/3661#issuecomment-1044340547 helps fixing that. Still with that change, with the introductory example we get ``` File "/Users/admin/prog/feed_filtering/.venv/lib/python3.10/site-packages/promptify/prompts/nlp/prompter.py",...
Hi, Thank you for such great work. Could you please add huggingface models or any other available models on the web to this package so that we can test it...
Hi, It would be great if you could add more comprehensive test cases and examples so that the models can be more accurately evaluated. Moreover, there is no explanation about...
Issue was in Question-Generation prompt, Need to check and fix it. Issue code: ``` from promptify import OpenAI from promptify import Prompter context = "The patient is a 93-year-old female...
It would bring a lot of value to the community to have this feature. Are you concidering it's implementation?
Could you please add documentation for examples, benchmarks, and API? It is somehow complicated to know the parameters of each model and function.
The response for the NER template is a string representation of a list of dictionaries. Currently this means the user first needs to parse and convert into list of dicts...
Hi many times while running this model in pycharm the model just freezes and don;t return the output. It works sometimes after restarting the kernel twice or thrice. Sometimes doesn't...
To Avoid Error: `*** SyntaxError: closing parenthesis '}' does not match opening parenthesis '['` For Output: ``` "[{'T': 'Age', 'E': '93-year-old'}, {'T': 'Gender', 'E': 'female'}, {'T': 'Medical Condition', 'E': 'chronic...
**14/7/2023 : successfully runing** `examples = [list of dictionnaries here as examples] data = list_of_product_names result = nlp_prompter.fit('ner.jinja', domain = 'ecommerce', text_input = f'{data}', labels = ["NAME", "WEIGHT","VOLUME","COUNT"], examples =...