I'm trying to build a keras model to classify text for 45 different classes. I'm a little confused about preparing my data for the input as required by google's BERT model.
Some blog posts insert data as a tf dataset with input_ids, segment ids, and mask ids, as in this guide, but then some only go with input_ids and masks, as in this guide.
Also in the second guide, it notes that the segment mask and attention mask inputs are optional.
Can anyone explain whether or not those two are required for a multiclass classification task?
If it helps, each row of my data can consist of any number of sentences within a reasonably sized paragraph. I want to be able to classify each paragraph/input to a single label.
I can't seem to find many guides/blogs about using BERT with Keras (Tensorflow 2) for a multiclass problem, indeed many of them are for multi-label problems.
I guess it is too late to answer but I had the same question. I went through huggingface code and found that if attention_mask and segment_type ids are None then by default it pays attention to all tokens and all the segments are given id 0.
If you want to check it out, you can find the code here
Let me know if this clarifies it or you think otherwise.
Related
I am doing a multi-label text classification using a pre-trained model of BERT. Here is an example of the prediction that has been made for one sentence-
pred_image
I want to get those words from the sentence on which the prediction has been made. Like this one - right_one
If anyone has any idea, Please enlighten me.
Multi-Label Text Classification (first image) and Token Classification (second image) are two different tasks for each which the model needs to be specifally trained for.
The first one returns a probability for each label considering the entire sentence. The second returns such predictions for each single word in the sentence while usually considering the rest of the sentence as context.
So you can not really use the output from a Text Classifier and use it for Token Classification because the information you get is not detailed enough.
What you can and should do is train a Token Classification model, although you obviously will need token-level-annotated data to do so.
i am working with bert for relation extraction from binary classification tsv file, it is the first time to use bert so there is some points i need to understand more?
how can i get an output like giving it a test data and show the classification results whether it is classified correctly or not?
how bert extract features of the sentences, and is there a method to know what are the features that is chosen?
i used once the hidden layers and another time i didn't use i got the accuracy of not using the hidden layer higher than using it, is there an reason for that?
I’m trying to make French email sentences multilabel classification with certain categories such as commitmemt, proposition, meeting, request, subjectif, etc. .
The first problem I faced is that I don’t have labeled sentences rather I have french emails as dataset). Based on this I found the BC3 dataset (English emails) which has sentences annotated with some of labels listed above. So I came up with this approah; First finetune a bert multilingual on this BC3 dataset on multilabel classification task and then make a zero-shot transfert learning with the finetuned model (or simply use it in inference) on sentences of my French emails. What do you think about this approach?
So I started by prepropcessing the BC3 dataset and obtain 848 sentences, each of them with their occurences annotations according to each categorty. On the image below, the last 5 columns represent the number of time each annotator labeled a sentence for a specific label.
Are those 848 samples enough to fine tune a Bert multilingual model?
I try to fine tune by representing category as on the image below.
With one epoch, BATCH_SIZE = 4, the loss function did’t converge, rather it oscillates between 0.79 and 0.34.
What kind of advices would you give the solve this kind of problem?
Thanks.
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 am newbie on data science so my question might be basic.
I have a dataset. 1st column is comments of people about issues (as text), 2nd columns is class/labels of that failure (as text). There are many failure types on my 2nd column.
I want to train a model. When another comment is entered and explained the issue, model should classify the failure.
Can I use Keras Sequential model? Or should I use different model? If you can share a link which can be related my question, I will be appreciate.
You can use Keras Sequential model for sure. Now as a beginner, try using Dense layers, and you can also use Convolutional Neural Networks for it...
and btw try using the tensorflow.keras.preprocessing.text Tokenizer to label each word as numbers so the machine can understand.
For more information, search on Google for text classification and search for the Tokenizer.