Aspect-classification for different data

[kabirwalia8300]

Hi again, I'm trying to use my own data set in the pipeline following the steps you have listed. I've split the documents into json files as.When I run the argument-classification script, i get the following error:

$ python argument_classification.py --topic culture --index arguana
Start classifying sentences for topic "culture" from doc_id_start 0 with MAX_FILE_SIZE 200000, and FILTER_TOPIC set "True"". Writing to ../../training_data/arguana/culture/
0%| | 0/170 [00:00<?, ?it/s]
string indices must be integers
Crashed at doc_id 0

This error tends to arise from using a string index in dictionaries/JSONs incorrectly. Is there anything I need to change in the construction of the json files?

EDIT
Fixed the issue. I was creating the JSON files incorrectly.

import spacy
nlp = spacy.load('en_core_web_sm')
import json

def makeFile(lst):
d = {'sents':lst}
with open('doc.json', 'w') as filehandle:
json.dump(d, filehandle)

def makeSentList(var):
about_doc = nlp(var)
sentences = list(about_doc.sents)
sentences = [str(x) for x in sentences]
return sentences

One can use the above functions to make a JSON file for the particular task. var is the document to process and lst is the output of the makeSentList function.

[v1nc3nt27]

Hi, thanks for sharing the solution, this should create documents in the correct format. Is the pipeline working now?

[kabirwalia8300]

Hello. I'm having issues fine-tuning the model due to RAM-related memory issues. I'm working on Colab with 25GB RAM allocation. Any suggestions on changing the pipelining of TF-records for training? and what number of iterations (hence batch size) would you recommend?

[v1nc3nt27]

Hey, with 25GB a batch size of 3 should work fine.

With your second question, you mean the number of iteration steps? It depends on how many samples you have. If you have lots of samples (like 100k+), then I'd suggest to run just one epoch first and see how the results turn out.

[kabirwalia8300]

I see. Thank you for your response - i think i'm not facing a memory issue anymore but i got the following:

FailedPreconditionError: Error while reading resource variable encoder/encoder_layer_7/multi_head_attention_7/dense_44/kernel/Adagrad from Container: localhost. This could mean that the variable was uninitialized. Not found: Resource localhost/encoder/encoder_layer_7/multi_head_attention_7/dense_44/kernel/Adagrad/N10tensorflow3VarE does not exist.

I looked it up and found the solution(s) given in this issue: tensorflow/tensorflow#28287

Since the training is a little different, can you suggest would be the right way to apply the solution given?

[v1nc3nt27]

Hey, you mean you changed the training code? I think without the context or seeing the modified code I cannot really help with this. If this appears without changing the code, please make sure you have the correct tf version given in the requirements.txt.

[kabirwalia8300]

Hey thank you for the feedback. I was training on Colab and forgot to apply the patch. That fixed it.