model = AutoModel.from_pretrained(model_type)# new tokensnew_tokens = ["new_token"]# check if the tokens are already in the vocabularynew_tokens =set(new_tokens) -set(tokenizer.vocab.keys())# add the tokens to the tokenizer vocabularytokenizer.add_tokens(list(new_tokens))# add new, ra...
from transformers import AutoTokenizer, AutoModel # pick the model type model_type = "roberta-base" tokenizer = AutoTokenizer.from_pretrained(model_type) model = AutoModel.from_pretrained(model_type) # new tokens new_tokens = ["new_token"] # check if the tokens are already in the vocabular...
These tokenizers handle unknown tokens by splitting them up in smaller subtokens. This allows for the text to be processed, but the special meaning of the token might be hard to capture for the model this way. Also splitting words up in many subtokens leads to longer sequences of tokens ...
Add logic to decide use huggingface tokenizer or sentence piece tokenizer. It can support models using huggingface tokenizer like Falcon and Deepseek Coder add hf tokenzier support 8c2be34 CyberTimon commented Nov 28, 2023 Thank you so much for this work @DOGEwbx . I'm waiting for deepse...
tiktoken_model_name: Optional[str] = None """tiktoken is not supported for upstage.""" tokenizer_name: Optional[str] = "upstage/solar-1-mini-tokenizer" """huggingface tokenizer name. Solar tokenizer is opened in huggingface https://huggingface.co/upstage/solar-1-mini-tokenizer"""@...
I use huggingface transformers build a new moe model, when I use AutoForCasualModel to load the model, there is no suitable model structure to load it, in this case, the parameter couldn't be load correctly. To evaluate the performance of this model, I have to add a new style model ...
image_special_token: '<__dj__image>' # the special token that represents an image in the text. In default, it's "<__dj__image>". You can specify your own special token according to your input dataset. audio_key: 'audios' # key name of field to store the list of sample audio...
经过初步调研发现在英文世界的大模型,预训练数据都来自互联网爬取的全网数据,在英文世界有 Common crawl 这样的组织来维护这类全网爬虫数据集;也有 huggingface 这种非常好的社区,组织起 NLP 领域的模型 datasets 分享。 而在中文世界,似乎没有特别公开的大规模语料数...
I want the vocabulary to include certain tokens that might or might not exist in the training dataset. from datasets import load_dataset from tokenizers import models, pre_tokenizers, trainers, Tokenizer, Regex # Dataset ds = load_dataset('HuggingFaceFW/fineweb', streaming = True)['train'] ...
does_t5_have_sep_token()print('Done\a') but feels hacky. refs: https://github.com/huggingface/tokenizers/issues/247 https://discuss.huggingface.co/t/how-to-add-all-standard-special-tokens-to-my-tokenizer-and-model/21529 seems useful:https://huggingface.co/docs/transforme...