question-answering | repository contains curated PyTorch implementations | Chat library

I'm getting this error TypeError: list indices must be integers or slices, not str in this piece of code (I'm creating a game and I'm assigning some string to images.

I'm trying to get the game to add some score when the right image is hit and for that I need to get this to work and I'm a bit lost right now.

I would appreciate it if you help me!

I am trying to ease my job. I need to do some analysis on the answers BERT gives me for thousands of files. My main objective is to iterate through every file and ask A question.

I have been trying to automate it with the following code

I am trying to implement a QA system using models from huggingface. One thing I do not understand is, when I don't specify which pre-trained model I am using for question-answering, is the model chosen at random?

My Question: How to make my 'question-answering' model run, given a big (>512b) .txt file?

Context: I am creating a question answering model with the word embedding model BERT from google. The model works fine when I import a .txt file with a few sentences, but when the .txt file exceeds the limit of 512b words as context for the model to learn, the model won't answer my questions.

My Attempt to resolve issue: I set a max_length at the encoding part, but that does not seem to solve the problem (my attempt code is below).

I am trying to create a question-answering model with the word embedding model BERT from google. I am new to this and would really want to use my own corpus for the training. At first I used an example from the huggingface site and that worked fine:

I have a NLP model trained on Pytorch to be run in Jetson Xavier. I installed Jetson stats to monitor usage of CPU and GPU. When I run the Python script, only CPU cores work on-load, GPU bar does not increase. I have searched on Google about that with keywords of " How to check if pytorch is using the GPU?" and checked results on stackoverflow.com etc. According to their advices to someone else facing similar issue, cuda is available and there is cuda device in my Jetson Xavier. However, I don’t understand why GPU bar does not change, CPU core bars go to the ends.

I don’t want to use CPU, it takes so long to compute. In my opinion, it uses CPU, not GPU. How can I be sure and if it uses CPU, how can I change it to GPU?

Note: Model is taken from huggingface transformers library. I have tried to use cuda() method on the model. (model.cuda()) In this scenario, GPU is used but I can not get an output from model and raises exception.

Here is the code:

I quite new to Google Cloud Platform and I am trying to train a model with TPU. I follow this tutorial to set up the TPU with Google Colab. All the code below follows the tutorial.

This is the step I have done:

I am working on a French Question-Answering model using huggingface transformers library. I'm using a pre-trained CamemBERT model which is very similar to RoBERTa but is adapted to french.

Currently, i am able to get the best answer candidate for a question on a text of my own, using the QuestionAnsweringPipeline from the transformers library.

Here is an extract of my code.

My setup has an NVIDIA P100 GPU. I am working on a Google BERT model to answer questions. I am using the SQuAD question-answering dataset, which gives me questions, and paragraphs from which the answers should be drawn, and my research indicates this architecture should be OK, but I keep getting OutOfMemory errors during training:

ResourceExhaustedError: OOM when allocating tensor with shape[786432,1604] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node dense_3/kernel/Initializer/random_uniform/RandomUniform}}]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

Below, please find a full program that uses someone else's implementation of Google's BERT algorithm inside my own model. Please let me know what I can do to fix my error. Thank you!