I downloaded it:
nltk.download('all')
Now I wish to add the dataset to my classifier. Does anyone know a tutorial?
Related
so this is a specific question involving two Tensorflow text classification tutorials on tensorflow.org. Sorry if this is the wrong place to ask.
Basically, there are two tutorials, one is "Classify Text with BERT" https://www.tensorflow.org/text/tutorials/classify_text_with_bert
And the other is "Fine-tuning a BERT model"
https://www.tensorflow.org/text/tutorials/fine_tune_bert
In these two tutorials, it describes preprocessing data. In "Classify Text with BERT", they use a preprocessing model provided by Tensorflow Hub, but in "Fine-tuning a BERT model", they implement python code which tokenizes the data and encodes it and some other stuff. Basically, it seems like the latter method is a lot more complicated than the former.
My question is, why does one tutorial use a preprocessing model provided, while the other actually implements python code? Is there a difference between the two tutorials that requires them to use their specific preprocessing methods?
Thank you!
I would like to use AllenNLP Interpret (code + demo) with a PyTorch classification model trained with HuggingFace (electra base discriminator). Yet, it is not obvious to me, how I can convert my model, and use it in a local allen-nlp demo server.
How should I proceed ?
Thanks in advance
If your task is binary classification, you can look at the BoolQ example in https://github.com/allenai/allennlp-models/blob/main/training_config/classification/boolq_roberta.jsonnet. You can change that configuration to use a different model (such as Electra).
We also just put some new documentation out for the Interpret functionality: https://guide.allennlp.org/interpret
To give you a more specific answer, I'll need to know some more details, like what the task is you're trying to solve, how you trained the original model, etc.
Good day, I am a student that is interested in NLP. I have come across the demo on AllenNLP's homepage, which stated that:
The model is a simple LSTM using GloVe embeddings that is trained on the binary classification setting of the Stanford Sentiment Treebank. It achieves about 87% accuracy on the test set.
Is there any reference to the sample code or any tutorial that I can follow to replicate this result, so that I can learn more about this subject? I am trying to obtain a Regression Output (Instead of classification).
I hope that someone can point me in the right direction.. Any help is much appreciated. Thank you!
AllenAI provides all code for examples and lib opensource on Git, including AllenNLP.
I found exactly how the example was run here: https://github.com/allenai/allennlp/blob/master/allennlp/tests/data/dataset_readers/stanford_sentiment_tree_bank_test.py
However, to make it a Regression task, you'll have to tweak directly on Pytorch, which is the underlying technology for AllenNLP.
I am doing a deep learning report that specifically uses the tensorflow library to identify and target the subject, and I want to find the same image as the identifying image, what should I do?
I have a tutorial on identifying images similar to the CNN model but with RFCN (rfcn_resnet101_coco) I have not done it yet. May everyone help.
Thank you very much
I am newbie to WEKA. I have written a program to classify Text using LibSVM and WEKA.
The code is available in BitBucket Repo .
Can somebody tell me whether I am in the right path.
Bet regards
Jaggu
You might want to evaluate your approach to see if your 'on the right path'. Use cross validation to see if the models trained by your approach yield the performance you want.