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QUESTION
I'm using bert pre-trained model for question and answering. It's returning correct result but with lot of spaces between the text
The code is below :
...ANSWER
Answered 2021-Jun-15 at 17:14You can just use the tokenizer decode function:
QUESTION
I'm currently working on a seminar paper on nlp, summarization of sourcecode function documentation. I've therefore created my own dataset with ca. 64000 samples (37453 is the size of the training dataset) and I want to fine tune the BART model. I use for this the package simpletransformers which is based on the huggingface package. My dataset is a pandas dataframe. An example of my dataset:
My code:
...ANSWER
Answered 2021-Jun-08 at 08:27While I do not know how to deal with this problem directly, I had a somewhat similar issue(and solved). The difference is:
- I use fairseq
- I can run my code on google colab with 1 GPU
- Got
RuntimeError: unable to mmap 280 bytes from file : Cannot allocate memory (12)
immediately when I tried to run it on multiple GPUs.
From the other people's code, I found that he uses python -m torch.distributed.launch -- ...
to run fairseq-train, and I added it to my bash script and the RuntimeError is gone and training is going.
So I guess if you can run with 21000 samples, you may use torch.distributed to make whole data into small batches and distribute them to several workers.
QUESTION
I have a pyTorch-code to train a model that should be able to detect placeholder-images among product-images. I didn't write the code by myself as I am very unexperienced with CNNs and Machine Learning.
My boss told me to calculate the f1-score for that model and i found out that the formula for that is ((precision * recall)/(precision + recall))
but I don't know how I get precision and recall. Is someone able to tell me how I can get those two parameters from that following code?
(Sorry for the long piece of code, but I didn't really know what is necessary and what isn't)
ANSWER
Answered 2021-Jun-13 at 15:17You can use sklearn to calculate f1_score
QUESTION
I have png image. I want to upsample it using bicubic interpolation. I found this function in pytorch:
...ANSWER
Answered 2021-Jun-13 at 12:16You can do this
QUESTION
I want to force the Huggingface transformer (BERT) to make use of CUDA.
nvidia-smi showed that all my CPU cores were maxed out during the code execution, but my GPU was at 0% utilization. Unfortunately, I'm new to the Hugginface library as well as PyTorch and don't know where to place the CUDA attributes device = cuda:0
or .to(cuda:0)
.
The code below is basically a customized part from german sentiment BERT working example
...ANSWER
Answered 2021-Jun-12 at 16:19You can make the entire class inherit torch.nn.Module
like so:
QUESTION
I have a NET like (exemple from here)
...ANSWER
Answered 2021-Jun-07 at 14:26The most naive way to do it would be to instantiate both models, sum the two predictions and compute the loss with it. This will backpropagate through both models:
QUESTION
I'm new on PyTorch and I'm trying to code with it
so I have a function called OH
which tack a number and return a vector like this
ANSWER
Answered 2021-Apr-30 at 23:19the problem is that you are receiving a tensor on the act function on the Network and then save it as a tensor just remove the tensor in the action like this
QUESTION
If I need to freeze the output layer of this model which is doing the classification as I don't need it.
...ANSWER
Answered 2021-Jun-11 at 15:33You are confusing a few things here (I think)
Freezing layersYou freeze the layer if you don't want them to be trained (and don't want them to be part of the graph also).
Usually we freeze part of the network creating features, in your case it would be everything up to self.head
.
After that, we usually only train bottleneck (self.head
in this case) to fine-tune it for the task at hand.
In case of your model it would be:
QUESTION
After instantiating a 2D convolution with conv = nn.Conv2d(8, 8, 3, bias=False)
, whose member bias
should be None
, is it able to give conv
a legal bias again (whether with random initialization or determined values)?
I observed that bias in other default convolution modules is of the type Parameter
, so I suspect there are extra procedures beyond simply conv.bias = torch.tensor(...)
to make the new bias legal for conv
.
ANSWER
Answered 2021-Jun-12 at 12:48Yes, it is possible to set the bias of the conv layer after instantiating. You can use the nn.Parameter class to create bias parameter and assign to conv object's bias attribute.
To show this I have created a simple Conv2d layer and assigned zero to the weights and ones to bias.
QUESTION
I'm trying to solve this Error:
ModuleNotFoundError: No module named 'torch'
I did the installation of Pytorch using this command:
conda install pytorch -c pytorch
but when I import torch I got the message above.
ANSWER
Answered 2021-Jun-09 at 20:40Do you have two Python versions installed on your machine?
The error says it could not find the module, maybe it was installed in another version. If that is the case, try to open your python folder where conda.exe is located and run directly especifying that conda file.
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