roberta | Steam Play compatibility tool to run adventure games | Game Engine library

There is nothing wrong with having a loss that is the sum of two individual losses, here is a small proof of principle adapted from the docs:

As discussed on GitHub: The problem is that you are constructing a 7-way classifier on top of BERT. Even though the BERT model will be identical, the 7-way classifier on top of it is randomly initialized every time.

BERT itself does not come with a classifier. That has to be fine-tuned for your data.

Have you tried to export after defining the operator for onnx? Something along the lines of the following code by Huawei.

On another note, when loading a model, you can technically override anything you want. Putting a specific layer to equal your modified class that inherits the original, keeps the same behavior (input and output) but execution of it can be modified. You can try to use this to save the model with changed problematic operators, transform it in onnx, and fine tune in such form (or even in pytorch).

This generally seems best solved by the onnx team, so long term solution might be to post a request for that specific operator on the github issues page (but probably slow).

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.

Try transforming your data into the correct format, before feeding it to model.fit:

I've adapted a solution based on this discussion from the Huggingface forums. Basically, you have to specify the names of the modules/pytorch layers that you want to freeze.

In your particular case of T5, I started by looking at the model summary:

You can use the Trainer from transformers to train the model. This trainer will also need you to specify the TrainingArguments, which will allow you to save checkpoints of the model while training.

Some of the parameters you set when creating TrainingArguments are:

• save_strategy: The checkpoint save strategy to adopt during training. Possible values are:
  • "no": No save is done during training.
  • "epoch": Save is done at the end of each epoch.
  • "steps": Save is done every save_steps.
• save_steps: Number of updates steps before two checkpoint saves if save_strategy="steps".
• save_total_limit: If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir.
• load_best_model_at_end: Whether or not to load the best model found during training at the end of training.

One important thing about load_best_model_at_end is that when set to True, the parameter save_strategy needs to be the same as eval_strategy, and in the case it is “steps”, save_steps must be a round multiple of eval_steps.

The vectors setting is not related to the transformer or what you're trying to do.

In the new config, you want to use the source option to load the components from the existing pipeline. You would modify the [component] blocks to contain only the source setting and no other settings: