I uesd TFBertModel and Tensorflow model to combined and training with Hugging Face transformers.I want to save the best model of val_accuracy of each epoch.I used ‘tensorflow checkpoint’, but I got the error.It seems like transformers can not be used in pytorch.How do I save the best model of each epoch with transformers bert in ..
I am looking to build a pipeline that applies the hugging-face BART model step-by-step. Once I have built the pipeline, I will be looking to substitute the encoder attention heads with a pre-trained / pre-defined encoder attention head. The pipeline I will be looking to implement is as follows: Tokenize input Run the tokenized input ..
I am looking to approach topic-aware modelling from a different angle. I am looking to provide the model with a topic e.g. ‘Car’ and the model to prioritise its summary based on this keyword. For absolute clarity, if I was to input the same article (A body of text describing cars and their country of ..
This is my code from transformers import Trainer trainer = Trainer( model=model, data_collator=data_collator, args=training_args, compute_metrics=compute_metrics, train_dataset=dataset_prepared["train"], eval_dataset=dataset_prepared["test"], tokenizer=processor.feature_extractor, ) trainer.train() And the error that I am getting is as follows: ————————————————————————— RuntimeError Traceback (most recent call last) <ipython-input-29-3435b262f1ae> in <module>() —-> 1 trainer.train() 5 frames /usr/local/lib/python3.7/dist-packages/transformers/trainer.py in train(self, resume_from_checkpoint, trial, **kwargs) 1051 tr_loss += self.training_step(model, ..
I was looking at the documentation at https://huggingface.co/pierreguillou/bert-base-cased-squad-v1.1-portuguese import transformers from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("pierreguillou/bert-base-cased-squad-v1.1-portuguese") model = AutoModelForQuestionAnswering.from_pretrained("pierreguillou/bert-base-cased-squad-v1.1-portuguese") but it raises an exception: OSError: Can’t load config for ‘pierreguillou/bert-base-cased-squad-v1.1-portuguese’. Make sure that: – ‘pierreguillou/bert-base-cased-squad-v1.1-portuguese’ is a correct model identifier listed on ‘https://huggingface.co/models’ – or ‘pierreguillou/bert-base-cased-squad-v1.1-portuguese’ is the correct path to a directory ..
Following this stackoverflow question: Outputting attention for bert-base-uncased with huggingface/transformers (torch) I am leveraging Hugging-face’s Bart model for summarising text. In particular, I am going to follow their suggestion of using ‘Bart for conditional generation’ I am trying to identify, analyse and tweak the encoder attention layer for articles depending on different topics. The code ..
I am trying to fine tune Wav2Vec2 model for medical vocabulary. When I try to run the following code on my VS Code Jupyter notebook, I am getting an error, but when I run the same thing on Google Colab, it works fine. from transformers import Wav2Vec2ForCTC model = Wav2Vec2ForCTC.from_pretrained( "facebook/wav2vec2-base", gradient_checkpointing=True, ctc_loss_reduction="mean", pad_token_id=processor.tokenizer.pad_token_id, ) ..
I am using Huggingface library and transformers to find whether a sentence is well-formed or not. I am using a masked language model called XLMR. I first tokenize my sentence, and then mask each word of the sentence one by one, and then process the masked sentences and find the probability that the predicted masked ..
I am doing sentiment analysis, and I was wondering how to show the other sentiment scores from classifying my sentence: "Tesla’s stock just increased by 20%." I have three sentiments: positive, negative and neutral. This is my code, which contains the sentence I want to classify: pip install happytransformer from happytransformer import HappyTextClassification happy_tc = ..
I am using a transformer model for my closed domain QnA. it answers most of the questions when the question is clear. if we slightly change the question it’s not able to pick the answer correctly . I am building extractive QA using Haystack gitrepo.This repo For example: If I Ask " how to register ..