I have some imbalanced data which I need to classify. I want to use SMOTE to balance it. But I don't really understand how to use it since I have BERT multiple inputs. Do I need to use it for input_ids? Or attention_masks? Or both? Also, a piece of code would be really useful :)
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I was not able to understand one thing , when it says "fine-tuning of BERT", what does it actually mean:
Are we retraining the entire model again with new data.
Or are we just training top few transformer layers with new data.
Or we are training the entire model but considering the pretrained weights as initial weight.
Or there is already few layers of ANN on top of transformer layers which is only getting trained keeping transformer weight freeze.
Tried Google but I am getting confused, if someone can help me on this.
Thanks in advance!
I remember reading about a Twitter poll with similar context, and it seems that most people tend to accept your suggestion 3. (or variants thereof) as the standard definition.
However, this obviously does not speak for every single work, but I think it's fairly safe to say that 1. is usually not included when talking about fine-tuning. Unless you have vast amounts of (labeled) task-specific data, this step would be referred to as pre-training a model.
2. and 4. could be considered fine-tuning as well, but from personal/anecdotal experience, allowing all parameters to change during fine-tuning has provided significantly better results. Depending on your use case, this is also fairly simple to experiment with, since freezing layers is trivial in libraries such as Huggingface transformers.
In either case, I would really consider them as variants of 3., since you're implicitly assuming that we start from pre-trained weights in these scenarios (correct me if I'm wrong).
Therefore, trying my best at a concise definition would be:
Fine-tuning refers to the step of training any number of parameters/layers with task-specific and labeled data, from a previous model checkpoint that has generally been trained on large amounts of text data with unsupervised MLM (masked language modeling).
I have a Neural Network with five inputs for a classification task. Two inputs out of those five are very important and have a direct relationship to the classification task. Therefore, I need to prioritize those two inputs within the network and give less priority to the other three. Is there a way in the neural network to facilitate my requirement?
If training works well, the NN should automatically pick up what's most important for your classification. That's the entire point of a NN (or ML in general); so that you don't have to manually tell it what's more important and what's not. After learning, you can verify that the model indeed does learn the correct order of importance between the features.
You can use any model explanation technique for this. ELI5, SHAP or LIME are some examples. All these will tell you if your model did indeed learn that the features that you know are important is actually important to the network.
You probably shouldn't try to manually incorporate such biases into the network (unless you have a very good reason for doing so, like incorporating spatial information of images via CNNs). Trust the learning xD
I want to fine tune BERT on a specific domain. I have texts of that domain in text files. How can I use these to fine tune BERT?
I am looking here currently.
My main objective is to get sentence embeddings using BERT.
The important distinction to make here is whether you want to fine-tune your model, or whether you want to expose it to additional pretraining.
The former is simply a way to train BERT to adapt to a specific supervised task, for which you generally need in the order of 1000 or more samples including labels.
Pretraining, on the other hand, is basically trying to help BERT better "understand" data from a certain domain, by basically continuing its unsupervised training objective ([MASK]ing specific words and trying to predict what word should be there), for which you do not need labeled data.
If your ultimate objective is sentence embeddings, however, I would strongly suggest you to have a look at Sentence Transformers, which is based on a slightly outdated version of Huggingface's transformers library, but primarily tries to generate high-quality embeddings. Note that there are ways to train with surrogate losses, where you try to emulate some form ofloss that is relevant for embeddings.
Edit: The author of Sentence-Transformers recently joined Huggingface, so I expect support to greatly improve over the upcoming months!
#dennlinger gave an exhaustive answer. Additional pretraining is also referred as "post-training", "domain adaptation" and "language modeling fine-tuning". here you will find an example how to do it.
But, since you want to have good sentence embeddings, you better use Sentence Transformers. Moreover, they provide fine-tuned models, which already capable of understanding semantic similarity between sentences. "Continue Training on Other Data" section is what you want to further fine-tune the model on your domain. You do have to prepare training dataset, according to one of available loss functions. E.g. ContrastLoss requires a pair of texts and a label, whether this pair is similar.
I believe transfer learning is useful to train the model on a specific domain. First you load the pretrained base model and freeze its weights, then you add another layer on top of the base model and train that layer based on your own training data. However, the data would need to be labelled.
Tensorflow has some useful guide on transfer learning.
You are talking about pre-training. Fine-tuning on unlabeled data is called pre-training and for getting started, you can take a look over here.
I don't have much experience with training neural networks. I have 4 variable vectors as input and I have respectively 3 variable output vector. I want to create a neural network that takes these inputs and outputs which have some unknown correlation(might not be linear) between them and train. So that when I put previously untrained data through it should predict the correlated output.
I was wondering,
What type of model should I use in such scenarios? Is it Restricted boltzmann machine, regression, GAN, etc?
What library is easiest to learn and implement for such a model? eg:- TensorFlow, PyTorch, etc
If images were involved which can be processed as fft arrays, would the model change.
I did find this answer, but I am not satisfied with it.
Please let me know if there are any functions or other points you would like me to know. Any help is much appreciated.
A multilayer perceprton is a good place to start.
Keras is the highest level/easiest to use library I have used.
If you are working with images or spatially structured data a convolutional neural network will probably work best.
I have a task to compare two images and check whether they are of the same class (using Siamese CNN). Because I have a really small data set, I want to use keras imageDataGenerate.
I have read through the documentation and have understood the basic idea. However, I am not quite sure how to apply it to my use case, i.e. how to generate two images and a label that they are in the same class or not.
Any help would be greatly appreciated?
P.S. I can think of a much more convoluted process using sklearn's extract_patches_2d but I feel there is an elegant solution to this.
Edit: It looks like creating my own data generator may be the way to go. I will try this approach.