I fine-tuned OpenAI model with some prompts following this documentation it succeeded and created a new model in the playground. How I can retrain (fine-tune) that same model with new prompts?
This is what I found out on Open AI Documentation
If you have already fine-tuned a model for your task and now have additional training data that you would like to incorporate, you can continue fine-tuning from the model. This creates a model that has learned from all of the training data without having to re-train from scratch.
i.e you can't train the already existing trained model again.
Related
I trained an Bert-based NER model using Pytorch framework by referring the below article.
https://www.depends-on-the-definition.com/named-entity-recognition-with-bert/.
After training the model using this approach, I saved that model using torch.save() method. Now, I want to retrain the model with new dataset.
Can someone please help me on how to perform retraining/transfer learning as I'm new to NLP and transformers.
Thanks in advance.
First, you can read the doc on pytorch loading, the model that can be very helpful to retrain the save model in a new dataset. I will provide the example of loading a save model. That can be very helpful to you
Original Doc :- https://pytorch.org/tutorials/beginner/saving_loading_models.html
Example code :- https://pythonguides.com/pytorch-load-model/
This two link are very helpful to train the new dataset on save model
I am working on a NLP model where the model identifies the ARGs given the PREDICATE.
I am using MaxEnt for the model.
My model works fine. I trained the model with a Train dataset, created all the features, and then tested it with a Test dataset.
I wanted to try this with some other package and not with MaxEnt.
Can someone suggest what else can I use?
I am using the GPT2 pre-trained model for a research project and when I load the pre-trained model with the following code,
from transformers.models.gpt2.modeling_gpt2 import GPT2Model
gpt2 = GPT2Model.from_pretrained('gpt2')
I get the following warning message:
Some weights of GPT2Model were not initialized from the model checkpoint at gpt2 and are newly initialized: ['h.0.attn.masked_bias', 'h.1.attn.masked_bias', 'h.2.attn.masked_bias', 'h.3.attn.masked_bias', 'h.4.attn.masked_bias', 'h.5.attn.masked_bias', 'h.6.attn.masked_bias', 'h.7.attn.masked_bias', 'h.8.attn.masked_bias', 'h.9.attn.masked_bias', 'h.10.attn.masked_bias', 'h.11.attn.masked_bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
From my understanding, it says that the weights of the above layers are not initialized from the pre-trained model. But we all know that attention layers ('attn') are so important in GPT2 and if we can not have their actual weights from the pre-trained model, then what is the point of using a pre-trained model?
I really appreciate it if someone could explain this to me and tell me how I can fix this.
The masked_bias was added but the huggingface community as a speed improvement compared to the original implementation. It should not negatively impact the performance as the original weights are loaded properly. Check this PR for further information.
I'm using faster_rcnn_resnet50 to train a model which will detect corrosions in images and I want to train a model from scratch instead of using transfer learning.
I don't know if this right but the reason I want to do this is that the already existing weights (which are trained on COCO) will affect my model trained on corrosion images.
One way I would like to do this is randomize or unfreeze the weights of the feature extractor on the resnet50 and then train the model on my images.
but there's no function or an option in the resnet50 config file to randomize or unfreeze weights.
I've made a new labelmap with a single label and tried it with transfer learning. It's working but I would like to have a model is trained just on my images and the previous weights shouldn't affect my predictions.
This is the first time I'm working with object detection and transfer learning. Will the weights of the pre-trained model on COCO affect my model which is trained on custom images of corrosion? How do you use tensorflow object-detection API without transfer learning?
I am working on GANS and I need to save the model after my working hours. And then I have to retrain that previously saved model again where it was saved. I am saving these three models to continue training later on.
Discriminator Model.h5
Generator Model.h5
Generator-on-Discriminator Model.h5
For these models, I am using perceptual loss and Wasserstein loss. But when I load_model to retrain that saved model again it encounters the following error.
Unknown loss function:wasserstein_loss
I have also tried Discriminator.compile(loss=Wasserstein loss) but this still not solving my issue. Can anyone of you please guide me over this and can tell me wither its possible to retrain a saved model using train_on_batch().
solved at my own
Defining custom_objects={'wassertein_loss':wassertein_loss} along with path while loading the model solved my issue. i.e.
Discriminator=load_model(model_path, custom_objects={'wassertein_loss':wassertein_loss} )