Next up are our classification layers. These will take the output from our BERT model and produce one of our three senti
These are pretty great results for such a simple output network. Further fine-tuning, the addition of CNNs, LSTMs, or other more expressive networks may improve our results even further.
Note: If training BERT layers too, try Adam optimizer with weight decay — which can help reduce overfitting and improve generalization [1]. I would recommend this article for understanding why.
If you are stuck on CPU, try out Google Colab — it’s a free, cloud-based notebook service provided by Google. Colab includes a GPU as standard — albeit not a particularly powerful one (but it is free).
First, we use the optimizer we all know and love. Next, we use category cross-entry and categorical accuracy for our loss and single metric. Because we have one-hot encoded our outputs, we use Categorical.
Our model summary shows the two input layers, BERT, and our final classification layers. We have a total of 108M parameters, of which just 100K are trainable because we froze the BERT parameters.
One of the main perks of buying a Linux laptop is that it comes optimized and there’s very little to tweak beyond your own personal visual preferences and such. So I have been just running on the stock kernel and had completely forgotten about the low latency option.
If you are following me here already, you will probably have noticed that I recently switched to a System 76 Lemur Pro as my daily driver and since running an OS made by the hardware vendor has obvious merits, I also went with Pop! OS when I made the switch. So-far, my experience has been great with very minimal gripes which I will save for another post.
If you are wanting to train the transformer parameters further, the final line is not necessary! We choose not to as BERT is already an incredibly well built and fine-tuned model. It would take a very long time to train, so for the likely minuscule performance increase — there’s little justification.
Alternatively, (although I found this to be detrimental) we can even use BERTs pre-pooled output tensors by swapping out last_hidden_state with pooler_output — but that is for another time.
Here we pull the outputs from distilbert and use a MaxPooling layer to convert the tensor from 3D to 2D — alternatively, use a 3D network (like convolutional or recurrent neural nets) followed by MaxPooling.
Around 3 years ago, triggered by getting a Lenovo X1C6 to replace my Macbook Air, I started tinkering with performance tweaks a bit more than before and one of the things I noticed is that if I use a low latency kernel, at least for my specific type of usage, the overall experience is a bit snappier. Since then, I have used the low latency kernel with Ubuntu on pretty much most of my daily drivers and the experience has been consistently better at least for what I do.
Alternatively, (although I found this to be detrimental) we can even use BERTs pre-pooled output tensors by swapping out last_hidden_state with pooler_output — but that is for another time.
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your mind active can improve your cognition skills in the long-term especially as you get older. “Regular mental challenges force you to think. Use it, or you’ll lose it,” says Constantine Lyketsos, PhD, professor at Johns Hopkins Bayview Medical Center.A few days ago, as I started reading up a bit on low latency kernels for audio production (what most people need the low latency kernel for) during my nightly wind down, I got a bit curious about the performance difference if I were to run the low latency kernel with Pop on my Lemur Pro. Then, today, I got a message from a fellow Lemur Pro owner asking about it so it kicked me over the edge and I decided to give it a go.
Pop! OS uses systemd-boot instead of grub so it took me a little while to figure out how to make the switch but after a few rounds of DuckDuckGo, I was able to lock in a pretty easy way to swap out the kernel.