![]() ![]() Anyway I did a test and doing what you did, but it works for me. from transformers import AutoTokenizer tokenizer = om_pretrained ('bert-base-cased') it should work correctly. from_pretrained ( 'SIC98/GPT2-python-code-generator') sequence = """# coding=utf-8 # Copyright 2020 The HuggingFace Inc. from_pretrained ( 'SIC98/GPT2-python-code-generator' ) model = GPT2LMHeadModel. English pre-trained GPT2 tokenizer (GPT2TokenizerFast) from the Transformers library (Hugging Face, version 3.0.0): it is a Fast GPT-2 BBPE tokenizer …from transformers import GPT2LMHeadModel, GPT2TokenizerFast tokenizer = GPT2TokenizerFast.We can use several versions of this GPT2 model, look at the transformers documentation for more details.Jul 3, 2020 from transformers import GPT2LMHeadModel, GPT2TokenizerFast. If you haven't done it yet, install the library: !pip install -Uq transformers Then let's import what will need: we will fine-tune the GPT2 pretrained model and fine-tune on wikitext-2 here.For this, we need the GPT2LMHeadModel (since we want a language model) and the GPT2Tokenizer to prepare the data. :/app# python src/translation/run.py Traceback (most recent call …First things first, we will need to install the transformers library. ![]() You should consider upgrading via the 'pip install -upgrade pip' command.From transformers import gpt2tokenizerfastimport lm_dataloader as lmdl from transformers import GPT2TokenizerFast jsonl_path = "test.jsonl" output = "my_dataset.lmd" tokenizer = om_pretrained('gpt2') lmdl.encode( jsonl_path, tokenize_fn=tokenizer.encode, tokenizer_vocab_size=len(tokenizer), output_prefix=output, eod_token=tokenizer.eos_token_id, )Feb 17, 2021 ![]()
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