We can create a model from AutoModel(TFAutoModel) function: from transformers import AutoModel model = AutoModel.from_pretrained('distilbert-base-uncase') In other hand, a model is created by Aut...

We know that BERT has a max length limit of tokens = 512, So if an article has a length of much bigger than 512, such as 10000 tokens in text How can BERT be used?

Trying to make a classifier for sentiments of texts with BERT model but getting ValueError : too many dimensions 'str' That is the DataFrame for values of train data; so they are train_labels 0 not...

I am finetuning the HuggingFace facebook/bart-large-mnli model to suit my need, I use the following parameters: training_args = TrainingArguments( output_dir=model_directory, # output directory n...

I have a pre-trained model from facebook/bart-large-mnli I used the Trainer in order to train it on my own dataset. model = BartForSequenceClassification.from_pretrained("facebook/bart-large-m...

I'm trying to finetune the Facebook BART model, I'm following this article in order to classify text using my own dataset. And I'm using the Trainer object in order to train: training_args = Traini...

I have a dataset and trying to convert it to topics using berTopic modeling but the problem is, i cant get all the docoments of a topic. berTopic is only return 3 docoments per topic. topic_model =...

I'm playing around with sklearn and NLP for the first time, and thought I understood everything I was doing up until I didn't know how to fix this error. Here is the relevant code (largely adapted ...

Is stopwords removal ,Stemming and Lemmatization necessary for text classification while using Spacy,Bert or other advanced NLP models for getting the vector embedding of the text ? text="The ...

If I want to implement a classifier using the sklearn library. Is there a way to save the model or convert the file into a saved tensorflow file in order to convert it to tensorflow lite later?

I'm doing different text classification experiments. Now I need to calculate the AUC-ROC for each task. For the binary classifications, I already made it work with this code: scaler = StandardScal...

I have a one-dimensional array with large strings in each of the elements. I am trying to use a CountVectorizer to convert text data into numerical vectors. However, I am getting an error saying: ...

I am doing a text classification and I have very imbalanced data like Category | Total Records Cate1 | 950 Cate2 | 40 Cate3 | 10 Now I want to over sample Cate2 and Cate3 so it at least have 40...

As my classifier yields about 99% accuracy on test data, I am a bit suspicious and want to gain insight in the most informative features of my NB classifier to see what kind of features it is learn...

So I have few words without labels but I need to classify them into 4-5 categories. I can visibly say that this test set can be classified. Although I do not have training data so I need to use a p...

Being new to the "Natural Language Processing" scene, I am experimentally learning and have implemented the following segment of code: from transformers import RobertaTokenizer, RobertaFo...

I am using Scikit-learn for text classification. I want to calculate the Information Gain for each attribute with respect to a class in a (sparse) document-term matrix. the Information Gain is def...

I have a dataset with paragraphs that I need to classify into two classes. These paragraphs are usually 3-5 sentences long. The overwhelming majority of them are less than 500 words long. I would l...

I am using GloVe as part of my research. I've downloaded the models from here. I've been using GloVe for sentence classification. The sentences I'm classifying are specific to a particular domain, ...

I am wondering if I can be able to use OpenAI GPT-3 for transfer learning in a text classification problem? If so, how can I get start on it using Tensorflow, Keras.

I've read post which explains how the sliding window works but I cannot find any information on how it is actually implemented. From what I understand if the input are too long, sliding window can ...

I have 3 questions: 1) The confusion matrix for sklearn is as follows: TN | FP FN | TP While when I'm looking at online resources, I find it like this: TP | FP FN | TN Which one should I co...

I am writing a classifier for web pages, so I have a mixture of numerical features, and I also want to classify the text. I am using the bag-of-words approach to transform the text into a (large) n...

I trained a supervised model in FastText using the Python interface and I'm getting weird results for precision and recall. First, I trained a model: model = fasttext.train_supervised("train.t...

I am doing text classification using a linear SVC model from sklearn. Now I want to visualize which words/tokens have the highest impact on the classification decision by using SHAP (https://github...