t2.micro instances are great for lightweight applications but do not have GPUs attached. Look for the g4-, g5-, and px-series servers and use one of the DL AMIs. Single GPU servers should work for starters, then you can simply upgrade the instance if you need parallel compute.

Something to consider as well, you're only charged for wall time on these servers when they're up so do as much of your development locally, then spin up the instance, pull your repo into your EC2, train, export model/checkpoints to S3, shut down EC2 (this can all be done in a script).

I bring this up because the cost seems high for a 24h usage cycle, but the A10 (g5) and above are really powerful and may actually be cheaper due to reduced training times.

Breaking even on a 4090 would take around 6-9 months at 8h/day using a single-GPU g5. Personally, I'm grabbing a 4070Ti when it comes out since I'm not so concerned with training times (12G is "ok") for personal projects and I don't want to upgrade my PSU.

Since I'm getting the down vote love here let me add some context to this. A human reader would see "white man" to mean Caucasian, not a man that is either dressed in all white clothing or has their skin painted white or has little melatonin. Annotating white in this context when training an NER would not make sense contextually if the goal is to identify color entities; labeling "white-skinned/light-skinned" would make sense as a color annotation.

A Finnish accountant during tax season and a Finnish-American surfer in Hawaii probably have different levels of melatonin in their skin but are both "white" (racially).

Generative models are not really the most effective approach for NER. Using another example as someone else provided:

What are the colors in the following sentence: "The white man like his burgers medium rare. He doesn't mind getting the red blood on his new green shirt." Return as JSON.

The colors in the sentence "The white man like his burgers medium rare. He doesn't mind getting the red blood on his new green shirt." are:

White

Red

Green

Here is the same information in JSON format:

Copy code

{ "colors": [ "White", "Red", "Green" ] } 

I hope this helps! Let me know if you have any other questions.

A properly trained NER would not have made the mistake of labeling a racial token as a color.

spaCy has an NLP framework to train NER models, but it does not use NLG to do so.

https://spacy.io/usage/facts-figures