Questions tagged [pretraining]
The pretraining tag has no usage guidance.
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what is the difference between window size and context length of language model?
is window size and context length of language model one and the same thing?
******** following text is added as question with ONLY above text was not allowed *****
I am trying to understand how GPT ...
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How to use pretrained encoder for customized Unet
if you have a standard Unet encoder such as resnet50, then it's easy to add pertaining to it. for example:
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Further Training a pre-trained LLM
My goal is to use the general knowledge and language understanding of a pre-trained LLM and to continue training on a smaller domain specific corpus to improve the model's knowledge on the domain. ...
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This model is too slow. I'm looking for a good, fast-enough, out-of-the-box, pre-trained image classifier. Any tip?
I have been using this on a laptop without a GPU: https://github.com/pharmapsychotic/clip-interrogator
Currently it takes about 10s to classify a single image on my own computer.
I use ...
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Does the Transformer model has memory to store the state accross different data injection sequences(segments)?
I've trained a transformer model based on the pytorch tutorial: https://pytorch.org/tutorials/beginner/transformer_tutorial.html,
But I found I've difficulties to understant this model's input and ...
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Is there any concern for a pretrained model to overfitting to a fine-tuning task that has overlapping pretraining and training data?
Let's say my language model is pretrained on a general text corpus, and I want to use it for some specific downstream task that has it's datasets also included in the general corpus, is there any ...
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How is model training affected after randomizing the weights of an intermediate layer of a pre-trained model?
Assuming that I have a deep learning model (let's say a ResNet) pretrained on a given dataset (let's say it is ImageNet). I load that model and randomize the weights of one of the intermediate layers, ...
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How long is the generator pre-trained in SeqGAN?
I am reading up about SeqGAN and I am trying to understand the pretraining step better.
The authors claim they want to maximize the Maximum Likelihood Estimation on the dataset S by pretraining the ...
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Reusing a model, pretrained on 19 classes, for just one of those classes
I have a pretrained net for semantic segmentation, which has been trained on the cityscapes dataset and its 19 classes (Person, car, traffic sign, …). One of those is "Person". I am only ...
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Is there practice to train language-to-code transformer (multi-modal transformer) using uni-modal pretrained models-transformers?
Language-to-code transformation/generation require multiple skills - language and reasoning skills to digest the core problem from the natural language specification. And programming language ...
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Pretrain RoBERTa model with new data using PyTorch library
I've pretrained the RoBERTa model with new data using a 'simpletransformers' library:
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Is it possible to "fine-tune" a pre-trained logistic regression model?
Fine tuning is a concept commonly used in deep learning. We may have a pre-trained model and then fine-tune it to our specific task.
Does that apply to simple models, such as logistic regression?
For ...
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Pretrained vs. finetuned model
I have a doubt regarding terminology. When dealing with huggingface transformer models, I often read about "using pretrained models for classification" vs. "fine-tuning a pretrained ...
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how to improve recall by retraining a model on its feedback
I am creating a supervised model using sensitive and scarce data. For the sake of discussion, I've simiplified the problem statement by assuming that I'm creating a model for identifying dogs.
Let's ...
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Can I leave natural outliers in a dataset in training?
Can I leave unedited natural outliers in a dataset (outliers that have not appeared just because of mistyping of mistakes in the data)? Or should I also remove them or change them?
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test data is not a good representation of train data
I have predefined train and test sets. On generating some statistics like value_counts and checking the unique values, I feel that there is a 'lot' of difference between the distributions of the ...
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How long does it take to fine-tune XLNet?
XLNet takes a lot more time than BERT during pre-training. This results in XLNet performing better than BERT in over 20 NLP tasks. How long does XLNet take for fine-tuning (let's assume this is ...
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Using mathematical derivatives of input data to augment training input data
I'm thinking of how to design a basic feedforward neural network that would be able to predict future datapoints given past datapoints. I'm very new to neural network design so I'm wondering if there'...
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Fine-tuning pre-trained Word2Vec model with Gensim 4.0
With Gensim < 4.0, we can retrain a word2vec model using the following code:
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Working on an image classification project (microscopic images) , have some doubts [closed]
Currently, I am working on an image classification project. The data set contains very high resolution images taken via an electron microscope. Hence, I have few and limited instances.
I have done EDA ...
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What the differences between self-supervised/semi-supervised in NLP?
GPT-1 mentions both Semi-supervised learning and Unsupervised pre-training but it seems like the same to me. Moreoever, "Semi-supervised Sequence Learning" of Dai and Le also more like self-...
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What is the common practice for NLP or text mining for non-English?
A lot of natural language processing tools are pre-trained with corpus in English. What if ones need to analyze, say, Dutch text? The blogs I find online are mostly saying traslating text into English ...
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Where to get models with weights instead of only weights? What's the purpose of .h5 files?
I have downloaded .h5 files from qubvel/resnet and qubvel/efficientnet. I was trying to use some models as a backbone for my model but I'm getting the following ...
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Would there be any reason to pretrain BERT on specific texts?
So the official BERT English model is trained on Wikipedia and BookCurpos (source).
Now, for example, let's say I want to use BERT for Movies tag recommendation. Is there any reason for me to pretrain ...
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How to access GPT-3, BERT or alike?
I am interested in accessing NLP models mentioned in scientific papers, to replicate some results and experiment.
But I only see waiting lists https://openai.com/blog/openai-api/ and licenses granted ...
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Logic behind pre-trained weights and transfer learning
I am not sure about the logic behind how pre-trained weights make sense and translate into a new problem.
To be more specific; for example, in a object detection network, how would a model's weights ...
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Does finetuning BERT involving updating all of the parameters or just the final classification layer?
Currently learning and reading about transformer models, I get that during the pretraining stage the BERT model is trained on a large corpus via MLM and NSP. But during finetuning, for example trying ...
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Deploying multiple pre-trained model (tar.gz files) on Sagemaker in a single endpoint
We have followed the following steps:
Trained 5 TensorFlow models in local machine using 5 different training sets.
Saved those in .h5 format.
Converted those into tar.gz (Model1.tar.gz,...Model5.tar....
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Are there any objections to using the same (unlabelled) data for pre-training of a BERT-Based model and the downstream task?
I'm looking to train an Electra model using unlabelled data in a specific field. Are there any objections to using the same data for unsupervised learning and then using the same data downstream for ...
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Semantic segmentation with greyscale images
I'm trying to reproduce a research with greyscale images instead of colour images.
I have found that there are pre-trained networks, like VGG16, with ImageNet. But that dataset has colour images, and ...
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Question of pretraining text-generation task, it seems that pretraining is not work for a small model?
My task is to generate keywords from sentences.
I pretrain a text-generation model. I mask the sentences' tokens and predict the whole sentences' tokens.
Pretraining batch_size = 8 and step = 1000000
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