pytorch-sentiment-analysis | getting started with PyTorch and TorchText for sentiment | Machine Learning library
kandi X-RAY | pytorch-sentiment-analysis Summary
kandi X-RAY | pytorch-sentiment-analysis Summary
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
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QUESTION
I'm currently using this repo to perform NLP and learn more about CNN's using my own dataset, and I keep running into an error regarding a shape mismatch:
...ANSWER
Answered 2021-Apr-07 at 13:16Your issue is here:
QUESTION
I'm following a PyTorch tutorial which uses the BERT NLP model (feature extractor) from the Huggingface Transformers library. There are two pieces of interrelated code for gradient updates that I don't understand.
(1) torch.no_grad()
The tutorial has a class where the forward()
function creates a torch.no_grad()
block around a call to the BERT feature extractor, like this:
ANSWER
Answered 2020-Sep-08 at 10:27This is an older discussion, which has changed slightly over the years (mainly due to the purpose of with torch.no_grad()
as a pattern. An excellent answer that kind of answers your question as well can be found on Stackoverflow already.
However, since the original question is vastly different, I'll refrain from marking as duplicate, especially due to the second part about the memory.
An initial explanation of no_grad
is given here:
with torch.no_grad()
is a context manager and is used to prevent calculating gradients [...].
requires_grad
on the other hand is used
to freeze part of your model and train the rest [...].
Source again the SO post.
Essentially, with requires_grad
you are just disabling parts of a network, whereas no_grad
will not store any gradients at all, since you're likely using it for inference and not training.
To analyze the behavior of your combinations of parameters, let us investigate what is happening:
a)
andb)
do not store any gradients at all, which means that you have vastly more memory available to you, no matter the number of parameters, since you're not retaining them for a potential backward pass.c)
has to store the forward pass for later backpropagation, however, only a limited number of parameter (3 million) are stored, which makes this still manageable.d)
, however, needs to store the forward pass for all 112 million parameters, which causes you to run out of memory.
QUESTION
I'm coming from Keras to PyTorch. I would like to create a PyTorch Embedding layer (a matrix of size V x D
, where V
is over vocabulary word indices and D
is the embedding vector dimension) with GloVe vectors but am confused by the needed steps.
In Keras, you can load the GloVe vectors by having the Embedding layer constructor take a weights
argument:
ANSWER
Answered 2020-Jun-10 at 00:21When torchtext
builds the vocabulary, it aligns the the token indices with the embedding. If your vocabulary doesn't have the same size and ordering as the pre-trained embeddings, the indices wouldn't be guaranteed to match, therefore you might look up incorrect embeddings. build_vocab()
creates the vocabulary for your dataset with the corresponding embeddings and discards the rest of the embeddings, because those are unused.
The GloVe-6B embeddings includes a vocabulary of size 400K. For example the IMDB dataset only uses about 120K of these, the other 280K are unused.
QUESTION
I'm trying to fine-tune a model with BERT (using transformers
library), and I'm a bit unsure about the optimizer and scheduler.
First, I understand that I should use transformers.AdamW
instead of Pytorch's version of it. Also, we should use a warmup scheduler as suggested in the paper, so the scheduler is created using get_linear_scheduler_with_warmup
function from transformers
package.
The main questions I have are:
get_linear_scheduler_with_warmup
should be called with the warm up. Is it ok to use 2 for warmup out of 10 epochs?- When should I call
scheduler.step()
? If I do aftertrain
, the learning rate is zero for the first epoch. Should I call it for each batch?
Am I doing something wrong with this?
...ANSWER
Answered 2020-Feb-20 at 08:58I think it is hardly possible to give a 100% perfect answer, but you can certainly get inspiration from the way other scripts are doing it. The best place to start is the examples/
directory of the huggingface repository itself, where you can for example find this excerpt:
QUESTION
I am trying to modify the code in this Tutorial to adapt it to a multiclass data (I have 55 distinct classes). An error is triggered and I am uncertain of the root cause. The changes I made to this tutorial have been annotated in same-line comments.
One of two solutions would satisfy this questions:
(A) Help identifying the root cause of the error, OR
(B) A boilerplate script for multiclass classification using PyTorch LSTM
...ANSWER
Answered 2020-Apr-16 at 17:44The BucketIterator
sorts the data to make batches with examples of similar length to avoid having too much padding. For that it needs to know what the sorting criterion is, which should be the text length. Since it is not fixed to a specific data layout, you can freely choose which field it should use, but that also means you must provide that information to sort_key
.
In your case, there are two possible fields, text
and wage_label
, and you want to sort it based on the length of the text
.
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