Hello. I had a very similar question, and in all the internet, this post has been the closest I found to addressing the issue. So, I got an idea: Why not ask Chat GPT3? You may or may not wish to read the following discussion I had with AI on this subject, but I do highly suggest you try getting the answers you need FROM the AI itself!
how do i create a custom model for support questions on our website?
There are several steps involved in creating a custom model for support questions on a website:
- Collect and label a dataset of support questions and answers. This will be used to train the model.
- Preprocess the dataset by cleaning and normalizing the text, and splitting it into training and testing sets.
- Choose a model architecture and train it on the dataset using a machine learning library such as TensorFlow or PyTorch.
- Fine-tune the model by adjusting its hyperparameters and continuing to train it on the dataset until the desired level of accuracy is achieved.
- Test the model on a separate test set to evaluate its performance.
- Integrate the trained model into the website’s code, and use it to provide answers to support questions from users.
It is also important to monitor the performance of the model, and continuously improve the model by using the feedback from the customer and updating the dataset.
how should I label a dataset of support questions and answers?
Labeling a dataset of support questions and answers can be done in a few different ways, depending on the specific task and the desired level of granularity. Here are a few common techniques:
- Classification: Assign one or more predefined categories to each question, such as “billing,” “technical,” or “account management.” This approach is useful when the goal is to route incoming questions to the appropriate department or agent.
- Intent identification: Assign one or more predefined intents to each question, such as “cancel subscription,” “change password,” or “get refund.” This approach is useful when the goal is to understand the user’s overall goal or objective, rather than the specific topic of the question.
- Keyword tagging: Assign one or more predefined keywords or phrases to each question, such as “billing,” “password,” or “refund.” This approach is useful when the goal is to identify specific topics or entities mentioned in the question.
- Multi-labeling: Assign multiple labels to each question, using a combination of the above techniques. This approach can provide a more detailed understanding of the question and its intent.
The most important thing is to label your dataset in a way that makes sense for your specific use case and is useful for the model you plan to train. It’s also important to have a consistent and clear definition of the labels. If you’re working with a large dataset or a team, consider using tools like labelbox to manage the labelling process
how do i classify questions? can you give me an example?
Classifying questions involves assigning one or more predefined categories or labels to each question, based on its content and meaning. Here is an example:
- Suppose you are building a model to classify customer support questions for a software company. You might define the following categories: “Billing,” “Technical,” “Account Management,” and “Sales.”
- You have a question “How do I change my billing address?” This question can be classified into “Billing” category
- Another question “Why is my software not working properly?” This question can be classified into “Technical” category
There are several techniques that can be used to classify questions, including:
- Supervised learning: Train a model on a labeled dataset of questions and their corresponding categories. The model can then be used to classify new questions based on their similarity to the examples in the training dataset.
- Rule-based systems: Define a set of rules or heuristics that map specific words, phrases, or patterns in the question to a particular category. For example, a rule that maps any question containing the word “billing” to the “Billing” category.
- Hybrid systems: Combine the above techniques, for example, a rule-based system that is fine-tuned using a supervised learning algorithm.
It’s important to note that the accuracy of the classification model depends on the quality of the labeled dataset and the chosen algorithm. So, it’s important to have a large and diverse labeled dataset and to evaluate the model performance on a separate test set.
Can you give me an example of how to add a category to a dataset?
Certainly, here’s an example of how you might add a classification label to a dataset of support questions and answers:
Let’s say you have a dataset of customer support questions and answers in a CSV file, with the following columns: “question,” “answer,” and “category”.
question,answer,category
How do I change my billing address?,You can change your billing address by logging into your account and going to the settings page,BillingWhy is my software not working properly?,It seems that there might be a compatibility issue with your current operating system. Please check the system requirements and update accordingly,Technical
In this example, each row represents one support question and answer, and the “category” column contains the classification label. The label is one of the predefined categories that you have chosen, such as “Billing”, “Technical”, “Account Management”, and “Sales”.
You can add more classification labels to the dataset by adding more rows to the CSV file and assigning a label to each question. It’s important to make sure that the labels are assigned consistently and accurately, in order to ensure the quality of the dataset.
It’s also a good practice to split the dataset into a training set and a test set, so you can use the training set to train the model and evaluate its performance on the test set.
You can use a library like Pandas to read and manipulate the CSV file and other libraries like scikit-learn to train and evaluate the model.
can you show me how to format the above example as a jsonl prompt dataset?
Sure! Here’s an example of how you might format the above dataset as a JSONL (JSON Lines) file for use with a language model like GPT-3:
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{"prompt":"How do I change my billing address?", "label": "Billing"}
{"prompt":"Why is my software not working properly?", "label":"Technical"}
In this example, each line is a JSON object representing one support question, and the “prompt” key contains the question text, the “label” key contains the classification label. The label is one of the predefined categories that you have chosen, such as “Billing”, “Technical”, “Account Management”, and “Sales”.
You can add more classification labels to the dataset by adding more lines to the JSONL file and assigning a label to each question. It’s important to make sure that the labels are assigned consistently and accurately, in order to ensure the quality of the dataset.
It’s also a good practice to split the dataset into a training set and a test set, so you can use the training set to train the model and evaluate its performance on the test set.
You can use a library like json or jsonlines to read and manipulate the JSONL file and other libraries like HuggingFace’s transformers to fine-tune the language model using your dataset.
can you include the completion in your above example?
Sure! Here’s an example of how you might format the above dataset as a JSONL (JSON Lines) file for use with a language model like GPT-3 that includes the completion as well:
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{"prompt":"How do I change my billing address? ", "completion": "You can change your billing address by logging into your account and going to the settings page", "label": "Billing"}
{"prompt":"Why is my software not working properly? ", "completion":"It seems that there might be a compatibility issue with your current operating system. Please check the system requirements and update accordingly", "label":"Technical"}
In this example, each line is a JSON object representing one support question and answer, and the “prompt” key contains the question text, the “completion” key contains the answer, and the “label” key contains the classification label. The label is one of the predefined categories that you have chosen, such as “Billing”, “Technical”, “Account Management”, and “Sales”.
You can add more classification labels to the dataset by adding more lines to the JSONL file and assigning a label to each question and completion. It’s important to make sure that the labels are assigned consistently and accurately, in order to ensure the quality of the dataset.
It’s also a good practice to split the dataset into a training set and a test set, so you can use the training set to fine-tune the model and evaluate its performance on the test set.
You can use a library like json or jsonlines to read and manipulate the JSONL file and other libraries like HuggingFace’s transformers to fine-tune the language model using your dataset.
Me:
After days of trying to hunt down this information, this answered my immediate burning questions. And, never before had I seen the “categorization” option! I know some of you are reading this and saying “Duh!” But, it just occurred to me today to ask ChatGPT3 how to build a mini-ChatGPT3 subset. Duh!
I asked: