Category : huggingface-tokenizers

I have sequence classification problem, I want my RoBERTa-base-sequence classier to predict weather the answer for a particular question is valid or Invalid. I am already able to do this for answers which are of 1 or 2 tokens by training this https://huggingface.co/iarfmoose/bert-base-cased-qa-evaluator transformers model on Hotpot QA and Squad. But the problem arises when ..

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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 ..

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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, ) ..

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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 = ..

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I am following this tutorial here: https://huggingface.co/transformers/training.html – though, I am coming across an error, and I think the tutorial is missing an import, but i do not know which. These are my current imports: # Transformers installation ! pip install transformers # To install from source instead of the last release, comment the command ..

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First i create tokenizer as follow from tokenizers import Tokenizer from tokenizers.models import BPE,WordPiece tokenizer = Tokenizer(WordPiece(unk_token="[UNK]")) from tokenizers.trainers import BpeTrainer,WordPieceTrainer trainer = WordPieceTrainer(vocab_size=5000,min_frequency=3, special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]) from tokenizers.pre_tokenizers import Whitespace,WhitespaceSplit tokenizer.pre_tokenizer = WhitespaceSplit() tokenizer.train(files, trainer) from tokenizers.processors import TemplateProcessing tokenizer.token_to_id("[SEP]"),tokenizer.token_to_id("[CLS]") tokenizer.post_processor = TemplateProcessing( single="[CLS] $A [SEP]", pair="[CLS] $A [SEP] $B:1 [SEP]:1", special_tokens=[ ..

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I am trying to execute the following code: from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained(model_checkpoint) # Let’s see how to increase the vocabulary of Bert model and tokenizer tokenizer = BertTokenizer.from_pretrained(‘bert-base-uncased’) model = AutoModelForMaskedLM.from_pretrained(‘bert-base-uncased’) num_added_toks = tokenizer.add_tokens([‘token_1’]) print(‘We have added’, num_added_toks, ‘tokens’) model.resize_token_embeddings(len(tokenizer)) # Notice: resize_token_embeddings expect to receive the full size of the new ..

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