![]() If the model’s performance is not satisfactory, you can fine-tune it further by adjusting the hyperparameters such as the learning rate, batch size, and number of epochs. Recall = recall_score(actual_labels, generated_labels)į1 = f1_score(actual_labels, generated_labels) Precision = precision_score(actual_labels, generated_labels) # Convert the actual labels into a list of labelsĪctual_labels = test_data.tolist()Īccuracy = accuracy_score(actual_labels, generated_labels) # Convert the generated strings into a list of labels Here is an example of how to evaluate the model on the test set: from trics import accuracy_score, precision_score, recall_score, f1_score ![]() Test_strings = test_data.tolist()Įvaluate the performance of the model by calculating its accuracy, precision, recall, and F1 score. ![]() # Fine-tune the model on the training set # Convert the training set into a list of strings # Create a test set using the remaining data Train_data = data.sample(frac=0.8, random_state=123) # Create a training set by selecting a subset of the data Here is an example of how to fine-tune the model on a financial dataset: data = pd.read_csv("financial_data.csv") Model = openai.Model(engine=model_engine)įine-tune the GPT-3 model on financial data using a sample dataset. model_engine = "davinci" # You can choose any model from the list provided by OpenAI Load the pre-trained GPT-3 model by adding the below code. Import OpenAI and add your OpenAI API key by creating a python file and replacing with your actual API key: import openai Install the openai module in Python using the command “pip install openai”. Make sure have your Deep Learning Architecture setup properly. Here is a general guide on fine-tuning GPT-3 models using Python on Financial data.įirstly, you need to set up an OpenAI account and have access to the GPT-3 API. "text" : "\nWe have been in the business of providing our customers with the best quality products and services for over 40 years.Example on Fine Tuning a Model on Financial Data Prompt= "Summarize the following text:During the first quarter, we maintained a very safe environment with an RIR of 0.64, which is in line with our full year 2020 performance." , Openai.api_key = os.getenv( "OPENAI_API_KEY" ) Use command line !openai api completions.create -m ada:ft-tpisoftware-00-10-20 -p "Summarize the following text:During the first quarter, we maintained a very safe environment with an RIR of 0.64, which is in line with our full year 2020 performance." Summarize the following text:During the first quarter, we maintained a very safe environment with an RIR of 0.64, which is in line with our full year 2020 performance. !openai api fine_tunes.follow -i ft-3Wnb4hOrXU1FuQGDRfyvNWlzĪfter the fine-tuned model is created, wee can test our function. It takes time to put fine-tune job in queue and train a model. ![]() !openai api fine_tunes.create -t "prepared_train.jsonl" -v "prepared_val.jsonl" -m curie Import openai os.environ = 'YOUR_API_KEY' Let's get startedįirst, we need to install the OpenAI library: It should include a number of pairs of prompt and completion. Checking the prepared training and validation data. Generate new secret key and keep it safely.Ģ. In this tutorial, we will build our own fine-tuned GPT-3 model with provided training and validation dataset.
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