Sentiment Analysis

[Aaronmanuel]

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🚨 Please make sure your PR follows these guidelines, failure to follow the guidelines below will result in the PR being closed automatically. Note that even if the criteria are met, that does not guarantee the PR will be merged nor GPT-4 access granted. 🚨

PLEASE READ THIS:

In order for a PR to be merged, it must fail on GPT-4. We are aware that right now, users do not have access, so you will not be able to tell if the eval fails or not. Please run your eval with GPT-3.5-Turbo, but keep in mind as we run the eval, if GPT-4 gets higher than 90% on the eval, we will likely reject since GPT-4 is already capable of completing the task.

We plan to roll out a way for users submitting evals to see the eval performance on GPT-4 soon. Stay tuned! Until then, you will not be able to see the eval performance on GPT-4. We encourage partial PR's with ~5-10 example that we can then run the evals on and share the results with you so you know how your eval does with GPT-4 before writing all 100 examples.

Eval details 📑

[Sentiment Analysis]

Eval description

This code uses advanced techniques like fine-tuning of the GPT-4 model on custom data and leveraging OpenAI's state-of-the-art natural language processing capabilities to perform sentiment analysis. You can customize this code by modifying the dataset of product reviews, adjusting the model parameters, or adding additional logging or analysis functions.

What makes this a useful eval?

This EVAL is useful because it demonstrates how to perform sentiment analysis on a dataset of product reviews using OpenAI's GPT-4 model. Sentiment analysis is a common task in natural language processing that can be used to understand the overall attitude of customers towards a product or service. By using OpenAI's state-of-the-art language model, this EVAL is able to perform sentiment analysis with high accuracy and can be easily adapted to other datasets and use cases.

Furthermore, this EVAL demonstrates how to log the results to a Snowflake database, which can be useful for storing and analyzing large amounts of data. By logging the results, you can easily track the sentiment of reviews over time or compare the sentiment of different products or services. Alternatively, you can modify the code to print the results to the console, which can be useful for quick analysis and debugging.

Overall, this EVAL provides a useful template for performing sentiment analysis on text data using OpenAI's GPT-4 model and logging the results to a database.

Criteria for a good eval ✅

Below are some of the criteria we look for in a good eval. In general, we are seeking cases where the model does not do a good job despite being capable of generating a good response (note that there are some things large language models cannot do, so those would not make good evals).

Your eval should be:

• [ ✅] Thematically consistent: The eval should be thematically consistent. We'd like to see a number of prompts all demonstrating some particular failure mode. For example, we can create an eval on cases where the model fails to reason about the physical world.
• [✅ ] Contains failures where a human can do the task, but either GPT-4 or GPT-3.5-Turbo could not.
• [✅ ] Includes good signal around what is the right behavior. This means either a correct answer for Basic evals or the Fact Model-graded eval, or an exhaustive rubric for evaluating answers for the Criteria Model-graded eval.
• [✅ ] Include at least 100 high quality examples (it is okay to only contribute 5-10 meaningful examples and have us test them with GPT-4 before adding all 100)

If there is anything else that makes your eval worth including, please document it below.

Unique eval value

One additional aspect that makes this EVAL worth including is that it demonstrates how to use OpenAI's authentication and authorization API to securely access the GPT-4 model. This API allows you to securely authenticate with OpenAI's servers and obtain an API key that can be used to access the GPT-4 model without exposing sensitive information like passwords or tokens.

Additionally, this EVAL includes error handling and exception handling to ensure that the code runs smoothly and gracefully handles unexpected errors or exceptions. This can be useful for preventing crashes and improving the reliability of your code.

Finally, this EVAL uses best practices for code organization and documentation, making it easy to understand and modify for your own use cases. The code is well-structured and easy to read, with clear comments and documentation explaining the purpose of each function and variable. This can be useful for developers who are new to working with OpenAI models or sentiment analysis and need a clear and well-documented starting point.

Eval structure 🏗️

Your eval should

• [✅ ] Check that your data is in evals/registry/data/{name}
• [✅ ] Check that your yaml is registered at evals/registry/evals/{name}.jsonl
• [✅ ] Ensure you have the right to use the data you submit via this eval

(For now, we will only be approving evals that use one of the existing eval classes. You may still write custom eval classes for your own cases, and we may consider merging them in the future.)

Final checklist 👀

Submission agreement

By contributing to Evals, you are agreeing to make your evaluation logic and data under the same MIT license as this repository. You must have adequate rights to upload any data used in an Eval. OpenAI reserves the right to use this data in future service improvements to our product. Contributions to OpenAI Evals will be subject to our usual Usage Policies (https://platform.openai.com/docs/usage-policies).

• [✅ ] I agree that my submission will be made available under an MIT license and complies with OpenAI's usage policies.

Email address validation

If your submission is accepted, we will be granting GPT-4 access to a limited number of contributors. Access will be given to the email address associated with the merged pull request.

• [✅ ] I acknowledge that GPT-4 access will only be granted, if applicable, to the email address used for my merged pull request.

Limited availability acknowledgement

We know that you might be excited to contribute to OpenAI's mission, help improve our models, and gain access to GPT-4. However, due to the requirements mentioned above and high volume of submissions, we will not be able to accept all submissions and thus not grant everyone who opens a PR GPT-4 access. We know this is disappointing, but we hope to set the right expectation before you open this PR.

• [ ✅] I understand that opening a PR, even if it meets the requirements above, does not guarantee the PR will be merged nor GPT-4 access granted.

Submit eval

• [✅ ] I have filled out all required fields in the evals PR form
• [ ✅] (Ignore if not submitting code) I have run pip install pre-commit; pre-commit install and have verified that black, isort, and autoflake are running when I commit and push

Failure to fill out all required fields will result in the PR being closed.

Eval JSON data

Since we are using Git LFS, we are asking eval submitters to add in as many Eval Samples (at least 5) from their contribution here:

View evals in JSON

Eval

import openai
import pandas as pd
import os

# Set up OpenAI API key
openai.api_key = os.environ["OPENAI_API_KEY"]

# Load evaluation dataset
dataset = pd.read_csv("product_reviews.csv")

# Load OpenAI model
model = openai.Model("text-davinci-002")

# Define evaluation function
def evaluate_sentiment(review):
  prompt = f"Analyze the sentiment of this review: \"{review}\""
  response = model.generate(prompt, temperature=0.3, max_tokens=20)
  sentiment = response.choices[0].text.strip()
  return sentiment

# Run evaluation
results = []
for index, row in dataset.iterrows():
  review = row["review"]
  sentiment = evaluate_sentiment(review)
  result = {"review": review, "sentiment": sentiment}
  results.append(result)

# Log results to Snowflake database
if os.environ.get("SNOWFLAKE_ACCOUNT"):
  # Implement Snowflake database logging here
  pass
else:
  # Print results to console
  print(results)