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Category: Content Moderation
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How to Train Models with Hive AutoML
What is Hive AutoML?
Hive’s AutoML platform allows you to quickly train, evaluate, and deploy machine learning models for your own custom use cases. The process is simple — just select your desired model type, upload your datasets, and you’re ready to begin training!
Since we announced the initial release of our AutoML platform, we’ve added support for Large Language Model training. Now you can build everything from classification models to chatbots, all in the same intuitive platform. To illustrate how easy the model-building process is, we’ll walk through it step-by-step with each type of model. We’ll also provide a link to the publicly available dataset we used as an example so that you can follow along.
Training an Image Classification Model
First we’re going to create an Image Classification model. This type of model is used to identify certain subjects, settings, and other visual attributes in both images and videos. For this example, we’ll be using a snacks dataset to identify 20 different kinds of food (strawberries, apples, hot dogs, cupcakes, etc.). To follow along with this walkthrough, first download the images from this dataset, which are sorted into separate files for each label.
Formatting the Datasets
After downloading the image data, we’ll need to put this data in the correct format for our AutoML training. For Image Classification datasets, the platform requires a CSV file that contains one column for image URLs titled “image_url” and up to 20 other columns for the classification categories you wish to use. This requires creating publicly accessible links for each image in the dataset. For this example, all 20 of our food categories will be part of the same head — food type. To do this, we formatted our CSV as follows:
This particular dataset is within the size limitations for Image Classification datasets. When uploading your own dataset, it is crucial that you ensure it meets all of the sizing requirements and other specifications or the dataset upload will fail. These requirements can be found in our AutoML documentation.
Both test and validation datasets are provided as part of the snacks dataset. When using your own datasets, you can choose to upload a test dataset or to split off a random section of your training data to use instead. If you choose the latter, you will be able to select what percentage of that data you want you use as test data as you create your training.
Uploading the Datasets
Before we start building the model, we first need to upload both our training and test datasets to the “Datasets” section of our AutoML platform. This part of our platform validates each dataset before it can be used for training as well as stores all datasets to be easily accessed for future models. We’ll upload both the training and test datasets separately, naming them Snacks (Train) and Snacks (Test) respectively.
Creating a Training
To start building your model, we’ll head to our AutoML platform and select the “Create New Model” button. We’ll then be brought to a project setup page where we will be prompted to enter a project name and description. For Model Type, we’ll select “Image Classification.” On the right side of the screen, we can add our training dataset by selecting from our dataset library. We’ll select the datasets called Snacks (Train) and Snacks (Test) that we just uploaded.
And just like that, we’re ready to start training our model! To begin the training process, we’ll click the “Start Training Model” button. The model’s status will then shift to “Queued” and then “In Progress” while we train the model. This will likely take several minutes. When training is complete, the status will display as “Completed.”
Evaluating the Model
After model training is complete, the page for that project will show various performance metrics so that we can evaluate our model. At the top of the page we can select the head and, if desired, the class that we’d like to evaluate. We can also use the slide to control the confidence threshold. Once selected, you will see the precision, recall, and balanced accuracy.
Below that, you can view the precision/recall curve (P/R curve) as well as a confusion matrix that shows how many predictions were correct and incorrect per class. This gives us a more detailed understanding of what the model misclassified. For example, we can see here that two images of cupcakes were incorrectly classified as cookies — an understandable mistake as the two are both decorated desserts.
These detailed metrics can help us to know what categories to target if we want to train a better version of the model. If you would like to retrain your model, you can also click the “Update Model” to begin the training process again.
Deploying the Model
Even after the first time training this model, we’re pretty happy with how it turned out. We’re ready to deploy the model and start using it. To deploy, select the project and click the “Create Deployment” button in the top right corner. The project’s status will shift to “Deploying.” The deployment may take a few minutes.
Submitting Tasks via API
After the deployment is complete, we’re ready to start submitting tasks via API as we would any pre-trained Hive model. We can click on the name of any individual deployment to open the project on Hive Data, where we can upload tasks, view tasks, and access our API key. There is also a button to “Undeploy” the project, if we wish to deactivate it at any point. Undeploying a model is not permanent — we can redeploy the project if we later choose to.
To see a video of the entire training and deployment process for an Image Classification model, head over to our Youtube channel.
Training a Text Classification Model
We’ll now walk through that same training process in order to build a Text Classification model, but with a few small differences. Text classification models can be used to sort and tag text content by topic, tone, and more. For this example, we’ll use the Twitter Sentiment Analysis dataset posted by user carblacac on Hugging Face. This dataset consists of a series of short text posts originally published to Twitter and whether they have a negative (0) or positive (1) overall sentiment. To follow along with this walkthrough, you can download the dataset here.
Formatting the Datasets
For Text Classification datasets, our AutoML platform requires a CSV with the text data in a column titled “text_data” and up to 20 other columns that each represent classification categories, also called model heads. Using the Twitter Sentiment Analysis dataset, we only need to rename the columns like so:
The data consists of two sets, a training set with 150k examples and a test set with 62k examples. Before we upload our dataset, however, we must ensure that it fits our Text Classification dataset requirements. In the case of the training set, it does not fit those requirements — our AutoML platform only accepts CSV files that have 100,000 rows or less and this one has 150,000. In order to use this dataset, we’ll have to remove some examples from the set. In order to keep the number of examples for each class relatively equal, we removed 25,000 negative (0) examples and 25,000 positive (1) ones.
Uploading the Datasets
After fixing the size issue, we’re ready to upload our datasets. As is the case with all model types, we must first upload any datasets we are going to use before we create our training.
Creating a Training
After both the training and test datasets have been validated, we’re ready to start building your model. On our AutoML platform, we’ll click the “Create New Model” button and enter a project name and description. For our model type, this time we’ll select “Text Classification.” Finally, we’ll add our training and test datasets that we just uploaded.
We’re then ready to start training! This aspect of the training process is identical to the one shown above for an Image Classification model. Just click the “Start Training Model” button on the bottom right corner of the screen. When training is complete, the status will display as “Completed.”
Evaluating the Model
Just like in our Image Classification example, the project page will show various performance metrics after training is complete so that we can evaluate our model. At the top of the page we can select the head and, if desired, the class that we’d like to evaluate. We can also use the slide to control the confidence threshold. Once selected, you will see the precision, recall, and balanced accuracy.
Below the precision, recall, and balanced accuracy, you can view the precision/recall curve (P/R curve) as well as a confusion matrix that shows how many predictions were correct and incorrect per class. This gives us a more detailed understanding of what the model misclassified. For example, we can see here that while there were a fair amount of mistakes for each class, there were more cases in which a positive example was mistaken for a negative than the other way around.
While the results of this training are not as good as our Image Classification example, this is somewhat expected — sentiment analysis is a more complex and difficult classification task. While this model could definitely be improved by retraining with slightly different data, we’ll demonstrate how to deploy it. To retrain your model, however, all you need to do is click the “Update Model” button and begin the training process again.
Deploying the Model
Deploying your model is the exact same process as described above in the Image Classification example. After the deployment is complete, you’ll be able to view the deployment on Hive Data and access the API keys needed in order to begin using the model.
To see a video of the entire training and deployment process for a Text Classification model, head over to our Youtube channel.
Training a Large Language Model
Finally, we’ll walk through the training process for a Large Language Model (LLM). This process is slightly different from the training process for our classification model types, both in terms of dataset formatting and model evaluation.
Our AutoML platform supports two different types of LLMs: Text and Chat. Text models are geared towards generating passages of writing or lines of code, whereas chat models are built for interactions with the user, often in the format of asking questions and receiving concise, factual answers. For this example, we’ll be using the Viggo dataset uploaded by GEM to Hugging Face. To follow along with us as we build the model, you can download the training and test sets here.
Formatting the Datasets
This dataset supports the task of summarizing and restructuring text into a very specific syntax. All data is within the video game domain, and all prompts take the form of either questions or statements about various games. The goal of the model is to take these prompts, extract the main idea behind them, and reformat them. For example, the prompt “Guitar Hero: Smash Hits launched in 2009 but plays like a game from 1989, it’s just not good” becomes “give_opinion(name[Guitar Hero: Smash Hits], release_year[2009], rating[poor]).”
First, we’ll check to make sure this dataset is valid per our guidelines for AutoML datasets. The size is well under the limit of 50,000 rows with only around 5,000. All that needs to be done to make sure that the formatting is correct is make sure that the prompt is in a column titled “prompt” and the expected completion is in another column titled “completion.” All other columns can be removed. From this dataset, we will use the column “target” as “prompt” and the column “meaning_representation” as “completion.” The final CSV is as shown below:
Uploading the Datasets
Now let’s upload our datasets. We’ll be using both the training and test datasets from the Viggo dataset as provided here. After both datasets have been validated, we’re ready to train the model.
Creating a Training
We’ll head back to our Models page and select “Create New Model”. This time, the project type should be “Language Generative – Text”. We will then choose our training and test datasets from a list of ones that we’ve already uploaded to the platform. Then we’ll start the training!
Evaluating the Model
For Large Language Models, the metrics page looks a little different than it does for our classification models.
The loss measures how closely the model’s response matches the response from the test data, where 0 represents a perfect prediction, and a higher loss signifies that the prediction is increasingly far from the actual response sequence. If the response has 10 tokens, we let the model predict each of the 10 tokens given all previous tokens are the same and display the final numerical loss value.
You can also evaluate your model by interacting with it in what we call the playground. Here you can submit prompts directly to your model and view its response, allowing model evaluation through experimentation. This will be available for 15 days after model training is complete, and has a limit of 500 requests. If either the time or request limit is reached, you can instead choose to deploy the model and continue to use the playground feature with unlimited uses which will be charged to the organization’s billing account.
For our Viggo model, all metrics are looking pretty good. We entered a few prompts into the playground to further test it, and the results showed no issues.
Deploying the Model
The process to deploy a Large Language Model is the same as it is for our classification models. Just click “Create Deployment” and you’ll be ready to submit API requests in just a few short minutes.
To see a video of the entire training and deployment process for an LLM, head over to our Youtube channel.
Final Thoughts
We hope this in-depth walkthrough of how to build different types of machine learning models with our AutoML platform was helpful. Keep an eye out for more AutoML tutorials in the coming weeks, such as a detailed guide to Retrieval Augmented Generation (RAG), data stream management systems (DSMS), and other exciting features we support.
If you have any further questions or run into any issues as you build your custom-made AI models, please don’t hesitate to reach out to us at support@thehive.ai and we will be happy to help. To inquire about testing out our AutoML platform, please contact sales@thehive.ai.
Dataset Sources
All datasets that are linked to as examples in this post are publicly available for a wide range of uses, including commercial use. The snacks dataset and viggo dataset are both licensed under a Creative Commons Attribution Share-Alike 4.0 (CC BY-SA 4.0) license. They can be found on Hugging Face here and here. The Twitter Sentiment Analysis dataset is licensed under the Apache License, Version 2.0. It is available on Hugging Face here. None of these datasets may be used except in compliance with their respective license agreements.
Best-in-Class: Hive Model Benchmarks
What does it mean to be “best-in-class”?
We often refer to our models as “industry-leading” or “best-in-class,” but what does this actually mean in practice? How are we better than our competitors, and by how much? It is easy to throw these terms around, but we mean it — and we have the evidence to back it up. In this blog post, we’ll be walking through some of the benchmarks that we have run against similar products to show how our models outperform the competition.
Visual Moderation
First, let’s take a look at one of our oldest and most popular models: visual moderation. To compare our model to its major competitors, we ran a test set of NSFW, suggestive, and clean images through all models.
Visual moderation is a classification task — in other words, the model’s job is to classify each submitted image into one of several categories (in this case, NSFW or Clean). A popular and effective metric to measure performance in classification models is by looking at their precision and recall. Precision is the number of true positives (i.e., correctly identified NSFW images) over the number of predicted positives (images predicted to be NSFW). Recall is the number of true positives (correctly identified NSFW images) over the number of ground-truth positives (actual NSFW images).
There is a tradeoff between the two. If you predict all images to be NSFW, you will have perfect recall — you caught all the NSFW images! — but horrible precision because you incorrectly classified many clean images as NSFW. The goal is to have both high recall and high precision, no matter what confidence threshold is used.
With our visual moderation models, we’ve achieved this. We plotted the results of our test as a precision/recall curve, showing that even at high recall we maintain high precision and vice versa while our competitors fall behind us.
The above plot is for NSFW content detection. Our precision at 90% recall is nearly perfect at 99.6%, which makes our error rate a whopping 45 times lower than Public Cloud C. Even Public Clouds A and B, which are closer to us in performance, have error rates 12.5 times higher and 22.5 times higher than ours respectively.
We also benchmarked our model for suggestive content detection, or content that is inappropriate but not as explicit as our NSFW category. Hive’s error rate remains far below the other models, resting at 6 times lower than Public Cloud A and 12 times lower than Public Cloud C. Public Cloud B did not offer a similar category and thus could not be compared.
We only ran our test on NSFW/explicit imagery more broadly because our competitors do not have equivalent classes to ours for other visual moderation classes such as drugs, gore, and terrorism. This makes comparisons difficult, though it also in itself speaks to the fact that we offer far more classes than many of our competitors. With more than 90 subclasses, our visual moderation model far exceeds its peers in terms of the granularity of our results — we don’t just have classes for NSFW, but also for nudity, underwear, cleavage, and other smaller categories that offer our customers a more more in-depth understanding of their content.
Text Moderation
We used precision/recall curves to compare our text moderation model as well. For this comparison, we charted our performance across eight different classes. Hive outperforms all peer models on every single one.
Hive’s error rate on sexual content is 4 times lower than its closest competitor, Public Cloud B. Our other two competitors for that class both have error rates 6 times higher. The threat class boasts similar metrics, with Hive’s error rate between 2 and 4 times lower than all its peers.
Hive’s model for hateful content detection is on par with our competitors, remaining slightly ahead on all thresholds. Our model for bullying content does the same, with an error rate 2 times lower than all comparable models.
Hive is one of few companies to offer text moderation for drugs and weapons, and our error rates here are also worth noting — our only competitor has an error rate 4 and 8 times higher than ours for drugs and weapons respectively.
Hive also offers the child exploitation class, one that few others provide. With this class, we achieve an error rate 8 times lower than our only other major competitor.
Audio Moderation
For Audio Moderation, we evaluate our model using word error rate (WER), which is the gold-standard metric for a speech recognition system. Word error rate is the number of errors divided by the total number of words transcribed, and a perfect word error rate is 0. As you can see, we achieve the best or near-best performance across a variety of languages.
We excel across the board, with the lowest word error rate on the majority of the languages offered. On Spanish in particular, our word error rate is more than 4 times lower than Public Cloud B.
For German and Italian we are very close behind Public Cloud C and remain better than all other competitors.
Optical Character Recognition (OCR)
To benchmark our OCR model, we calculated the F-score for our model as well as several of our competitors. F-score is the harmonic mean of a model’s precision and recall, combining both of them into one measurement. A perfect F-score is 1. When comparing general F-scores, Hive excels as shown below.
We also achieve best-in-class or near-best performance when comparing by language, as shown in the graphs below. With some languages, we excel by quite a large margin. For Chinese and Korean in particular, Hive’s F-score is more than twice all of its competitors. We fall slightly behind in Hindi, yet still perform significantly better than Public Cloud A.
Demographics
We evaluated our age prediction model by calculating mean error, or how far off our age predictions were from the truth. Since the test dataset we used is labeled using age ranges and not individual numbers, mean error is defined as the distance in years from the closest end of the correct age range (i.e., guessing 22 for someone in the range 25-30 is an error of 3 years). A perfect mean error is 0.
As you can see from this distribution, Hive has a significantly lower mean error rate in the three lowest age buckets (0-2, 3-9, and 10-19). In the age range 0-2, our mean error rate is 11 times less than Public Cloud A’s. For the range 3-9 and 10-19, that difference becomes 5 times greater and 3 times greater respectively — still quite a large margin. Hive also excels notably at the oldest age bucket (70+), where our mean error rate is nearly 7 times less than Public Cloud A’s.
For a broader analysis, we compared our overall mean error across all age buckets, as well as the accuracy of our gender predictions.
AutoML
One of the newest additions to our product suite, our AutoML platform allows you to train image classification, text classification, and fine-tune large language models with your own custom datasets. To evaluate the effectiveness of this tool, we used the same test set to train models both on our platform and on competitor’s platforms and measured the performance of the resulting model.
For image classification, we used three different classification tasks to account for the fact that different tasks have different levels of inherent difficulty and thus may yield higher or lower performing models. We also used three different dataset sizes for each classification task in order to measure how well the AutoML platform is able to work with limited amounts of examples.
We compared the resulting models using balanced accuracy, which is the arithmetic mean of a model’s true positive rate and true negative rate. A perfect balanced accuracy is 100%.
As shown in the above tables, Hive achieves best or near-best accuracy across all sets. Our results are quite similar to Public Cloud B’s, pulling ahead on the product dataset. We fell to near-best performance on the smoking dataset, which is the most difficult of the three classification tasks. Even then, we remained within a few percentage points of the winner, Public Cloud B.
For text classification, we trained models for three different categories: sexual content, drugs, and bullying. The results are in the table below. Hive outperforms all competitors on all three categories using all dataset sizes.
Another important consideration when it comes to AutoML is training time. An AutoML tool could build accurate models, but if it takes an entire day to do so it still may not be a great solution. We compared the time it took to train Hive’s text classification tool for the drugs category, and found that our platform was able to train the model 10 times as fast as Private Company A and 32 times as fast as Public Cloud B. And for the smallest dataset size of 100 examples, we trained the model 18 times faster than Private Company A and 268 times faster than Public Cloud B. That’s a pretty significant speedup.
Measuring the performance of fine-tuned LLMs on our foundation model is a bit more complicated. Here we evaluate two different tasks: question answering and closed-domain classification.
To measure performance on the question answering task, we used a metric called token accuracy. Token accuracy indicates how many tokens are the same between the model’s response and the expected response from the test set. A perfect token accuracy is 100%. As shown below, our token accuracy is higher than our competitors or around the same for all dataset sizes.
This is also true for the classification task, where maintained roughly the same performance as Public Cloud A across the various dataset sizes. Below are the full results of our comparison.
Final Thoughts
As illustrated throughout this in-depth look into the performance of our models, we truly earn the title “best-in-class.” We conduct these benchmarks not just to justify that title, but more so as part of our constant effort to make our models the best that they can be. Reviewing these analyses helps us to identify our strengths, yes, but also our weaknesses and where we can improve.
If you have any questions about any of the benchmarks we’ve discussed here or any other questions about our models, please don’t hesitate to reach out to us at sales@thehive.ai.
The New York Times
3 Tips and Tricks to Building ML Models
Hive was thrilled to have our CTO Dmitriy present at the Workshop on Multimodal Content Moderation during CVPR last week, where we provided an overview of a few important considerations when building machine learning models for classification tasks. What are the effects of data quantity and quality on model performance? Can we use synthetic data in the absence of real data? And after model training is done, how do we spot and address bias in the model’s performance?
Read on to learn some of the research that has made our models truly best-in-class.
The Importance of Quality Data
Data is, of course, a crucial component in machine learning. Without data, models would have no examples to learn from. It is widely accepted in the field that the more data you train a machine learning model with, the better. Similarly, the cleaner that data is, the better. This is fairly intuitive — the basic principle is true for human learners, too. The more examples to learn from, the easier it is to learn. And if those examples aren’t very good? Learning becomes more difficult.
But how important is good, clean data to building a good machine learning model? Good data is not always easy to come by. Is it better to use more data at the expense of having more noise?
To investigate this, we trained a binary image classifier to detect NSFW content, varying the amount of data between 10 images and 100k images. We also varied the noise by flipping a percentage of the labels on between 0% and 50% of the data. We then plotted the balanced accuracy of the resulting models using the same test set.
The result? It turns out that data quality is more important than we may think. It was clear that, as expected, accuracy was the best when the data was both as large as possible (100k examples) and as clean as possible (0% noise). From there, however, the table gets more interesting.
As seen above, the model trained with only 10k data and no noise performs better than the model trained with ten times as much data at 100k and 10% noise. The general trend appears to be similar — clean data matters very much, and it can quickly tank performance even when using the maximum amount of data. In other words, less data is sometimes preferable to more data if it is cleaner.
We wondered how this would change with a more detailed classification problem, so we built a new binary image classifier. This time, we trained the model to detect images of smoking, which is detecting signal from a small part of an image.
The outcome, shown below, echoes the results from the NSFW model — clean data has a great impact on performance even with a very large dataset. But the quantity of data appears to be more important than it was in the NSFW model. While 5000 examples with no noise got around 90% balanced accuracy for the NSFW model, that same amount of noiseless data only got around 77% for the smoking classifier. The increase in performance, while still strongly tied to data quantity, was noticeably slower and only the largest datasets produced well-performing models.
It makes sense that quantity of data would be more important with a more difficult classification task. Data noise also remained a crucial factor for the models trained with more data — the 50k model with 10% noise performed about the same as the 100k model with 10% noise, illustrating once more that more data is not always better if it is still noisy.
Our general takeaways here are that while both data quality and quantity matter quite a bit, clean data is more important beyond a certain quantity threshold. This threshold is where performance increases begin to plateau as the data grows larger, yet noisy data continues to have significant effects on model quality. And as we saw by comparing the NSFW model and the smoking one, this quality threshold also changes depending on the difficulty of the classification task itself.
Training on Synthetic Data: Does it Help or Hurt?
So having lots of clean data is important, what can be done when good data is hard to find or costly to acquire? With the rise of AI image generation over the past few years, more and more companies have been experimenting with generated images to supplement visual datasets. Can this kind of synthetic data be used to train visual classification models that will eventually classify real data?
In order to try this out, we trained five different binary classification models to detect smoking. Three of the models were trained exclusively with real data (10k, 20k, and 40k examples respectively), one was a mix of real and synthetic images (10k real and 30k synthetic), and one was trained entirely on synthetic data (40k). Each datatest had an even split of 50% smoking and 50% nonsmoking examples. To evaluate the models, we used two balanced test sets: one with 4k real images and one with 4k of synthetic images. All synetic images were created using Stable Diffusion.
Looking at the precision and recall curves for the various models, we made an interesting discovery. Unsurprisingly, the largest of the entirely real datasets performed the best (40k). The one trained on 10k real images and 30k synthetic images performed significantly better than the one trained only on 10k real images.
These results suggest that while large amounts of real data are best, a mixture of synthetic and real data could in fact boost model performance when little data is available.
Keeping an Eye Out For Bias
After model training is finished, extensive testing must be done in order to make sure there aren’t any biases in the model results. Biases can come in the form of biases that exist in the real world and are thus often ingrained in real-world data, such as racial bias or gender bias, but can also come in the form of biases that occur in the data by coincidence.
A great example of how unpredictable certain biases can be came recently during a model training for NSFW detection, where the model started flagging many pictures of computer keyboards as false positives. Upon closer investigation, this occurred because many of the NSFW pictures in our training data were photos of computers whose screens were displaying explicit content. Since the computer screens were the focus of these images, keyboards were also often included, leading to the false association that keyboards are an indicator of NSFW imagery.
In order to correct this bias, we added more non-NSFW keyboard examples to the training data. Improving this bias in this way not only helps the model by addressing the bias itself, but also boosts general model performance. Of course, addressing bias is even more critical when dealing with data that carries current or historical biases against minority groups, thereby perpetuating them by ingraining them into future technology. The importance of detecting and correcting these biases cannot be overstated, since leaving them unaddressed carries a significant amount of risk beyond simply calling a keyboard NSFW.
Regardless of the type of bias, it’s important to note that biases aren’t always readily apparent. The original model prior to addressing the bias had a balanced accuracy of 80%, which is high enough that the bias may not have been immediately noticeable since errors weren’t extremely frequent. The takeaway here is thus not just that bias correction matters, but that looking into potential biases is necessary even when you might not think they’re there.
Takeaways
Visual classification models are in many ways the heart of Hive — they were our main launching point into the space of content moderation and AI-powered APIs more broadly. We’re continuously searching for ways to keep improving these models as the research surrounding them grows and evolves. Conclusions like those discussed here — the importance of clean data, particularly when you have lots of it, the possible use of synthetic data when real data is lacking, and the need to find and correct all biases (don’t forget about the unexpected ones!) — greatly inform the way we build and maintain our products.
Build Your Own Custom ML Models with Hive AutoML
We’re excited to announce Hive’s new AutoML tool that provides customers with everything they need to train, evaluate, and deploy customized machine learning models.
Our pre-trained models solve a wide range of use cases, but we will always be bounded by the number of models we can build. Now customers who find that their unique needs and moderation guidelines don’t quite match with any of our existing solutions can create their own, custom-built for their platform and easily accessible via API.
AutoML can be used to augment our current offerings or to create new models entirely. Want to flag a particular subject that doesn’t exist as a head in our Text Moderation API, or a certain symbol or action that isn’t part of our Visual Moderation? With AutoML, you can quickly build solutions for these problems that are already integrated with your Hive workflow.
Let’s walk through our AutoML process to illustrate how it works. In this example, we’ll build a text classification model that can determine whether or not a given news headline is satirical.
First, we need to get our data in the proper format. For text classification models, all dataset files must be in CSV format. One column should contain the text data (titled text_data) and all other columns represent model heads (classification categories). The values within each row of any given column represent the classes (possible classifications) within that head. An example of this formatting for our satire model is shown below:
The first page you’ll see on Hive’s AutoML platform is a dashboard with all of your organization’s training projects. In the image below, you’ll see how the training and deployment status of old projects are displayed. To create our satire classifier, we’re going to make a new project by hitting the “Create New Project” button in the top right corner.
We’ll then be prompted to provide a name and description for the project as well as training data in the form of a CSV file. For test data, you can either upload a separate CSV file or choose to randomly split your training data into two files, one to be used for training and the other for testing. If you decide to split your data, you will be able to choose the percentage that you would like to split off.
After all of that is entered, we are ready to train! Beginning model training is as easy as hitting a single button. While your model trains, you can easily view its training status on the Training Projects page.
Once training is completed, your project page will show an analysis of the model’s performance. The boxes at the top allow you to decide if you want to look at this analysis for a particular class or overall. If you’re building a multi-headed model, you can choose which head you’d like to evaluate as well. We provide precision, recall, and balanced accuracy for all confidence thresholds as well as a PR curve. We also display a confusion matrix to show how many predictions were correct and incorrect per class.
Once you’re satisfied with your model’s performance, select the “Create Deployment” to launch the model. Similarly to model training, deployment will take a few moments. After model deployment is complete, you can view the deployment in your Hive customer dashboard, where you can access your API key, view current tasks, as well as access other information as you would with our pre-trained models.
We’re very excited to be adding AutoML to our offerings. The platform currently supports both text and image classification, and we’re working to add support for large language models next. If you’d like to learn more about our AutoML platform and other solutions we’re building, please feel free to reach out to sales@thehive.ai or contact us here.
The Wall Street Journal
Flag AI-Generated Text with Hive’s New Classifier
Hive is excited to announce our new classifier to differentiate between AI-generated and human-written text. This model is hosted on our website as a free demo, and we encourage users to test out its performance.
The recent release of OpenAI’s ChatGPT model has raised questions about how public access to these kinds of large language models will impact the field of education. Certain school districts have already banned access to ChatGPT, and teachers have been adjusting their teaching methods to account for the fact that generative AI has made academic dishonesty a whole lot easier. Since the rise of internet plagiarism, plagiarism detectors have become commonplace at academic institutions. Now a need arises for a new kind of detection: AI-generated text.
Our AI-Generated Text Detector outperforms key competitors, including OpenAI itself. We compared our model to their detector, as well as two other popular AI-generated text detection tools: GPTZero and Writer’s AI Content Detector. Our model was the clear frontrunner, not just in terms of balanced accuracy but also in terms of false positive rate — a critical factor when these tools are deployed in an educational setting.
Our test dataset consisted of 242 text passages, including ChatGPT-generated text as well as human-written text. To ensure that our model behaves correctly on all genres of content, we included everything from casual writing to more technical and academic writing. We took special care to include texts written by those learning English as a second language, so as to be careful that their writing is not incorrectly categorized by our model due to differences in tone or wording. For these test examples, our balanced accuracy stands at an impressive 99% while the closest competitor is GPTZero with 83%. OpenAI got the lowest of the bunch, with only 73%.
Others have tried our model against OpenAI’s in particular, and they have echoed our findings. Following OpenAI’s classifier release, Mark Hachman at PCWorld published an article that suggested that those disappointed with OpenAI’s model should turn to Hive’s instead. In his own informal testing of our model, he praised our results for their accuracy as well as our inclusion of clear confidence scores for every result.
A large fear about using these sorts of detector tools in an educational setting is the potentially catastrophic impact of false positives, or cases in which human-written writing is classified as AI-generated. While building our model, we were mindful of the fact that the risk of such high-cost false positives is one that many educators may not want to take. In response, we prioritized lowering our false positive rate. On the test set above, our false positive rate is incredibly low, at 1%. This is compared to OpenAI’s at 12.5%, Writer’s at 46%, and GPTZeros at 30%.
Even with our low false positive rate, we do encourage that this tool be used as part of a broader process when investigating academic dishonesty and not as the sole decision maker. Just like plagiarism checkers, it is created to be a helpful screening tool and not a final judge. We are continuously working to improve our model, and any feedback is greatly appreciated. Large language models like ChatGPT are here to stay, and it is crucial to provide educators with tools they can use as they decide how to navigate these changes in their classrooms.
Financial Times