fine-tuning | Fine-tuning an already learned model | Machine Learning library

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I'm trying to find information on how to train a BERT model, possibly from the Huggingface Transformers library, so that the embedding it outputs are more closely related to the context o the text I'm using.

However, all the examples that I'm able to find, are about fine-tuning the model for another task, such as classification.

Would anyone happen to have an example of a BERT fine-tuning model for masked tokens or next sentence prediction, that outputs another raw BERT model that is fine-tuned to the context?

Thanks!

I would like to fine-tune already fine-tuned BertForSequenceClassification model with new dataset containing just 1 additional label which hasn't been seen by model before.

By that, I would like to add 1 new label to the set of labels that model is currently able of classifying properly.

Moreover, I don't want classifier weights to be randomly initialized, I'd like to keep them intact and just update them accordingly to the dataset examples while increasing the size of classifier layer by 1.

The dataset used for further fine-tuning could look like this:

I am new to BERT and try to learn BERT Fine-Tuning for Text Classification via a coursera course https://www.coursera.org/projects/fine-tune-bert-tensorflow/

Based on the course, I would like to compare the text classification performance between BERT-12 and BERT-24 using 'SGD' and 'ADAM' optimizer respectively.

I found that when I use BERT-12, the result is normal. However, when switching to BERT-24, though the accuracy is good (9X%), the recall and precision value are extremely low (even close to zero).

May I know if there are anything wrong with my code?

Also, in order to improve the precision and recall, should I add more dense layers and change the activation functions? And what are the optimal learning rate values that I should use?

I am attempting to train an object detection model using Tensorflow's Object Detection API 2 and Tensorflow 2.3.0. I have largely been using this article as a resource in preparing the data and training the model.

Most articles which use the Object Detection API download a pre-trained model from the Tensorflow model zoo prior to fine-tuning.

The Tensorflow Model Zoo is a set of links on a Github page set up by the Object Detection team. When I click one such link (using Google Chrome), a new tab opens briefly as if a download is starting, then immediately closes and a download does not occur. Hyperlinks to other models I have found in articles also have not worked.

To anyone who has worked with fine-tuning using the Object Detection API: What method did you use to download a pre-trained model? Did the model zoo links work? If not, what resource did you use instead?

Any help is much appreciated.

I'm having trouble fine-tuning the decomposable-attention-elmo model. I have been able to download the model: wget https://s3-us-west-2.amazonaws.com/allennlp/models/decomposable-attention-elmo-2018.02.19.tar.gz. I'm trying to load the model and then fine-tune it on my data using the AllenNLP train command line command.

I also created a custom dataset Reader which is similar to the SNLIDatasetReader and it seems to be working well.

I created a .jsonnet file, similar to what is here, but I'm having trouble getting it to work.

When I use this version:

Hello together currently I´m trying to develop a model for contradicition detection. Using and fine-tuning a BERT Model I already got quite statisfactionary result but I think with with some other features I could get a better accuracy. I oriented myself on this Tutorial. After fine-tuning, my model looks like this:

Problem summary: The tensorflow custom object detector never starts fine-tuning when i follow the guide in docs. It doesn't throw an exception either.

What i've done: I have installed the object detector api and run a succesful test as according to the docs.

I then followed the guide about training a custom object detector algorithm here, including modifying the pipeline.config file. As per the guide i run

I follow the instruction of Fine-tuning BERT to build a model with my own dataset(It is kind of large, and greater than 20G), then take steps to re-cdoe my data and load them from tf_record files. The training_dataset I create has the same signature as that in the instruction