Instructions to use JTSJohnny/pipi0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use JTSJohnny/pipi0 with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://JTSJohnny/pipi0") - Notebooks
- Google Colab
- Kaggle
File size: 654 Bytes
e153144 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | from tokenizers import Tokenizer
from tokenizers.models import WordPiece
from tokenizers.pre_tokenizers import Whitespace
from tokenizers.trainers import WordPieceTrainer
def train_tokenizer(files, vocab_size=50000):
tokenizer = Tokenizer(WordPiece(unk_token=":OOV:"))
tokenizer.pre_tokenizer = Whitespace()
trainer = WordPieceTrainer(
vocab_size=vocab_size,
special_tokens=[":OOV:"],
continuing_subword_prefix="##",
)
tokenizer.train(files, trainer)
return tokenizer
def save_tokenizer(tokenizer, path):
tokenizer.save(path)
def load_tokenizer(path):
return Tokenizer.from_file(path)
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