bert | BERT serialization library for Ruby | Natural Language Processing library

It depends! Especially given your relatively vague setup description, it is not clear what loss will be used. But to start from the beginning, let's first check how the default compute_loss() function in the Trainer class looks like.

You can find the corresponding function here, if you want to have a look for yourself (current version at time of writing is 4.17). The actual loss that will be returned with default parameters is taken from the model's output values:

loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]

which means that the model itself is (by default) responsible for computing some sort of loss and returning it in outputs.

Following this, we can then look into the actual model definitions for BERT (source: here, and in particular check out the model that will be used in your Sentiment Analysis task (I assume a BertForSequenceClassification model.

The code relevant for defining a loss function looks like this:

Try using tf.saved_model.save to save your model:

The easiest way to resolve this is to patch SrlReader so that it uses PretrainedTransformerTokenizer (from AllenNLP) or AutoTokenizer (from Huggingface) instead of BertTokenizer. SrlReader is an old class, and was written against an old version of the Huggingface tokenizer API, so it's not so easy to upgrade.

If you want to submit a pull request in the AllenNLP project, I'd be happy to help you get it merged into AllenNLP!

Use this model instead.

You can use the Tokenizer __call__ method to join both sentences when encoding them.

In case you're using the PyTorch implementation, here is an example:

The error means you have no data_type column in your dataframe because you missed this step

As I don't exactly know what changes you have made in the code... I don't have idea about your dataset. But I can see that you are trying to train the whole datset with one epoch and passing the steps per epoch directly. I would recommend to write it like this

set some batch_size 2^n power (for example 16 or 32 or etc) if you don't want to batch the dataset just set batch_size to 1

There is a paper Masked Language Model Scoring that explores pseudo-perplexity from masked language models and shows that pseudo-perplexity, while not being theoretically well justified, still performs well for comparing "naturalness" of texts.

As for the code, your snippet is perfectly correct but for one detail: in recent implementations of Huggingface BERT, masked_lm_labels are renamed to simply labels, to make interfaces of various models more compatible. I have also replaced the hard-coded 103 with the generic tokenizer.mask_token_id. So the snippet below should work:

This seems to be what you’re after, using the descendant axis: