288 KiB
288 KiB
T5 model classification training
Links:
- Tensorboard training: https://tensorboard.dev/experiment/CgaWd9pATZeuquRT7TZp7w/#scalars
- Huggingface dataset edited: https://huggingface.co/datasets/Zombely/sst2-project-dataset
- Huggingface Trained model: https://huggingface.co/Zombely/t5-model
- Huggingface Tokenizer: https://huggingface.co/Zombely/t5-tokenizer
!pip install datasets transformers sentencepiece
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/ Collecting datasets Downloading datasets-2.9.0-py3-none-any.whl (462 kB) [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m462.8/462.8 KB[0m [31m8.3 MB/s[0m eta [36m0:00:00[0m [?25hCollecting transformers Downloading transformers-4.26.1-py3-none-any.whl (6.3 MB) [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m6.3/6.3 MB[0m [31m66.9 MB/s[0m eta [36m0:00:00[0m [?25hCollecting sentencepiece Downloading sentencepiece-0.1.97-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB) [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m1.3/1.3 MB[0m [31m49.0 MB/s[0m eta [36m0:00:00[0m [?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from datasets) (1.21.6) Collecting huggingface-hub<1.0.0,>=0.2.0 Downloading huggingface_hub-0.12.0-py3-none-any.whl (190 kB) [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m190.3/190.3 KB[0m [31m14.6 MB/s[0m eta [36m0:00:00[0m [?25hRequirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (2.25.1) Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5) Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0) Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from datasets) (23.0) Collecting responses<0.19 Downloading responses-0.18.0-py3-none-any.whl (38 kB) Collecting xxhash Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB) [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m213.0/213.0 KB[0m [31m10.7 MB/s[0m eta [36m0:00:00[0m [?25hRequirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6) Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3) Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2023.1.0) Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (4.64.1) Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (6.0) Collecting multiprocess Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB) [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m132.0/132.0 KB[0m [31m10.5 MB/s[0m eta [36m0:00:00[0m [?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1 Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB) [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m7.6/7.6 MB[0m [31m72.5 MB/s[0m eta [36m0:00:00[0m [?25hRequirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0) Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2) Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (2.1.1) Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.2.0) Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.4) Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3) Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1) Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2) Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.8.2) Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (4.4.0) Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (1.24.3) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2022.12.7) Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2.10) Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (4.0.0) Collecting urllib3<1.27,>=1.21.1 Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB) [2K [90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━[0m [32m140.6/140.6 KB[0m [31m10.8 MB/s[0m eta [36m0:00:00[0m [?25hRequirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2) Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0) Installing collected packages: tokenizers, sentencepiece, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets Attempting uninstall: urllib3 Found existing installation: urllib3 1.24.3 Uninstalling urllib3-1.24.3: Successfully uninstalled urllib3-1.24.3 Successfully installed datasets-2.9.0 huggingface-hub-0.12.0 multiprocess-0.70.14 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0
from datasets import load_dataset
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer, TrainingArguments, Trainer
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
import tensorflow as tf
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
import random
import time
import numpy as np
import datetime
import sklearn
from tqdm.notebook import tqdm
import os
Load data and transform dataset
def load_and_process_dataset():
dataset = load_dataset("sst2")
dataset.remove_columns('idx')
del dataset['test']
dataset['test'] = dataset['validation']
del dataset['validation']
split_dataset = dataset['train'].train_test_split(test_size=1600)
dataset['train'] = split_dataset['train']
dataset['validation'] = split_dataset['test']
return dataset
dataset = load_and_process_dataset()
dataset
WARNING:datasets.builder:Found cached dataset sst2 (/root/.cache/huggingface/datasets/sst2/default/2.0.0/9896208a8d85db057ac50c72282bcb8fe755accc671a57dd8059d4e130961ed5)
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DatasetDict({ train: Dataset({ features: ['idx', 'sentence', 'label'], num_rows: 65749 }) test: Dataset({ features: ['idx', 'sentence', 'label'], num_rows: 872 }) validation: Dataset({ features: ['idx', 'sentence', 'label'], num_rows: 1600 }) })
for key in dataset.keys():
dataset[key] = dataset[key].map(lambda x: {'source_text': f"sentiment-analysis: {x['sentence']}", 'target_text': 'positive' if x['label'] else 'negative'}).remove_columns(['idx', 'sentence', 'label'])
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train = dataset['train']
validation = dataset['validation']
test = dataset['test']
dataset['train'][0]
{'source_text': 'sentiment-analysis: smart , sassy and exceptionally charming ', 'target_text': 'positive'}
tokenizer = T5Tokenizer.from_pretrained("t5-small")
model = T5ForConditionalGeneration.from_pretrained("t5-small")
Downloading (…)ve/main/spiece.model: 0%| | 0.00/792k [00:00<?, ?B/s]
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/usr/local/lib/python3.8/dist-packages/transformers/models/t5/tokenization_t5.py:163: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5. For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`. - Be aware that you SHOULD NOT rely on t5-small automatically truncating your input to 512 when padding/encoding. - If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding. - To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value. warnings.warn(
Downloading (…)"pytorch_model.bin";: 0%| | 0.00/242M [00:00<?, ?B/s]
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model.cuda();
def tokenize(dataset):
input_ids = []
target_ids = []
attention_masks = []
for row in dataset:
encoding = tokenizer.encode_plus(
row['source_text'],
padding="max_length",
max_length=86,
truncation=True,
add_special_tokens = True,
return_attention_mask = True,
return_tensors="pt"
)
target_encoding = tokenizer.encode_plus(
row['target_text'],
padding="max_length",
max_length=2,
truncation=True,
add_special_tokens = True,
return_attention_mask = True,
return_tensors="pt",
)
input_ids.append(encoding['input_ids'])
target_ids.append(target_encoding['input_ids'])
attention_masks.append(encoding['attention_mask'])
return TensorDataset(torch.cat(input_ids, dim=0), torch.cat(attention_masks, dim=0), torch.cat(target_ids, dim=0))
train_tokenized = tokenize(train)
validation_tokenized = tokenize(validation)
test_tokenized = tokenize(test)
batch_size = 32
train_dataloader = DataLoader(
train_tokenized,
sampler = RandomSampler(train_tokenized),
batch_size = batch_size
)
validation_dataloader = DataLoader(
validation_tokenized,
sampler = SequentialSampler(validation_tokenized),
batch_size = batch_size
)
test_dataloader = DataLoader(
test_tokenized,
sampler = SequentialSampler(test_tokenized),
batch_size = batch_size
)
Main model training class
class T5Model:
def __init__(self, model, tokenizer, train_dataloader, val_dataloader, logs_dir_path, seed=42, epochs=3, lr=1e-4, eps=1e-8):
self.model = model
self.tokenizer = tokenizer
self.train_dataloader = train_dataloader
self.val_dataloader = val_dataloader
self.seed=seed
self.epochs = epochs
self.learning_rate = lr
self.eps = eps
self.logs_dir_path = logs_dir_path
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.optimizer = torch.optim.AdamW(model.parameters(), lr = lr, eps = eps)
self.tensorboard_writter()
self.init_seed()
def init_seed(self):
random.seed(self.seed)
np.random.seed(self.seed)
torch.manual_seed(self.seed)
torch.cuda.manual_seed_all(self.seed)
def tensorboard_writter(self):
if not os.path.exists(self.logs_dir_path):
os.mkdir(self.logs_dir_path)
self.summary_writer = tf.summary.create_file_writer(self.logs_dir_path)
def compute_metrics(self, target, preds):
return sklearn.metrics.accuracy_score(target, preds)
def _train(self, epoch_number):
print(f"Training epoch: {epoch_number}")
self.model.train()
train_accuracy_total = 0
pbar = tqdm(self.train_dataloader, total=len(self.train_dataloader))
for step, batch in enumerate(pbar):
b_input_ids = batch[0].to(self.device)
b_input_mask = batch[1].to(self.device)
b_labels = batch[2].to(self.device)
outputs = self.model(
input_ids=b_input_ids,
attention_mask=b_input_mask,
labels=b_labels
)
gen_output = self.model.generate(
input_ids = b_input_ids,
attention_mask = b_input_mask,
max_length=3,
num_beams=2,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True
)
preds = [self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in gen_output]
target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in b_labels]
loss = outputs[0]
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
accuracy = self.compute_metrics(target, preds)
train_accuracy_total += accuracy
pbar.set_description(f"Epoch {epoch_number+1}, step {step}, train loss {loss}, accuracy: {accuracy}")
with self.summary_writer.as_default():
tf.summary.scalar(name=f"Epoch: {epoch_number+1}, step/train_accuracy", data=accuracy ,step=step)
tf.summary.scalar(name=f"Epoch: {epoch_number+1}, step/train_loss", data=loss.cpu().detach().numpy(), step=step)
self.summary_writer.flush()
print(f"Epoch: {epoch_number+1}, Average train accuracy: {train_accuracy_total/len(self.train_dataloader)}")
def train(self):
print(f"Starting training, epochs: {self.epochs}")
for i in range(0, self.epochs):
self._train(i)
self.validate(i)
def validate(self, epoch_number):
print(f"Validation epoch: {epoch_number}")
self.model.eval()
val_accuracy_total = 0
pbar_val = tqdm(self.val_dataloader, total=len(self.val_dataloader))
for step, batch in enumerate(pbar_val):
b_input_ids = batch[0].to(self.device)
b_input_mask = batch[1].to(self.device)
b_labels = batch[2].to(self.device)
with torch.no_grad():
outputs = self.model(
input_ids=b_input_ids,
attention_mask=b_input_mask,
labels=b_labels
)
loss = outputs[0]
gen_output = self.model.generate(
input_ids = b_input_ids,
attention_mask = b_input_mask,
max_length=3,
num_beams=2,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True
)
preds = [self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in gen_output]
target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in b_labels]
accuracy_val = self.compute_metrics(target, preds)
val_accuracy_total += accuracy_val
pbar_val.set_description(f"Epoch {epoch_number+1}, val loss {loss}, accuracy: {accuracy_val}")
with self.summary_writer.as_default():
tf.summary.scalar(name=f"Epoch: {epoch_number+1}, step/val_accuracy", data=accuracy_val ,step=step)
tf.summary.scalar(name=f"Epoch: {epoch_number+1}, step/val_loss", data=loss.cpu().detach().numpy(), step=step)
self.summary_writer.flush()
print(f"Epoch: {epoch_number+1}, Average validation accuracy: {val_accuracy_total/len(self.val_dataloader)}")
def evaluate(self, test_dataloader):
self.model.eval()
total_test_acc = 0
for batch in tqdm(test_dataloader, total=len(test_dataloader)):
b_input_ids = batch[0].to(self.device)
b_input_mask = batch[1].to(self.device)
b_labels = batch[2].to(self.device)
with torch.no_grad():
gen_output = self.model.generate(
input_ids = b_input_ids,
attention_mask = b_input_mask,
max_length=3,
num_beams=2,
repetition_penalty=2.5,
length_penalty=1.0,
early_stopping=True
)
preds = [self.tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in gen_output]
target = [self.tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in b_labels]
total_test_acc += self.compute_metrics(preds, target)
print("Accuracy: ",total_test_acc / len(test_dataloader))
t5model = T5Model(model, tokenizer, train_dataloader, validation_dataloader, "./logs")
t5model.train()
Starting training, epochs: 3 Training epoch: 0
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Epoch: 1, Average train accuracy: 0.8825947167188043 Validation epoch: 0
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Epoch: 1, Average validation accuracy: 0.926875 Training epoch: 1
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Epoch: 2, Average train accuracy: 0.9252577916811494 Validation epoch: 1
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Epoch: 2, Average validation accuracy: 0.936875 Training epoch: 2
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Epoch: 3, Average train accuracy: 0.9391720252577916 Validation epoch: 2
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Epoch: 3, Average validation accuracy: 0.93875
t5model.evaluate(test_dataloader)
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Accuracy: 0.9129464285714286
!tensorboard dev upload --logdir logs --name t5-sst2
2023-02-13 11:49:46.367397: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia 2023-02-13 11:49:46.367509: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia 2023-02-13 11:49:46.367529: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. ***** TensorBoard Uploader ***** This will upload your TensorBoard logs to https://tensorboard.dev/ from the following directory: logs This TensorBoard will be visible to everyone. Do not upload sensitive data. Your use of this service is subject to Google's Terms of Service <https://policies.google.com/terms> and Privacy Policy <https://policies.google.com/privacy>, and TensorBoard.dev's Terms of Service <https://tensorboard.dev/policy/terms/>. This notice will not be shown again while you are logged into the uploader. To log out, run `tensorboard dev auth revoke`. Continue? (yes/NO) yes To sign in with the TensorBoard uploader: 1. On your computer or phone, visit: https://www.google.com/device 2. Sign in with your Google account, then enter: ZBRH-SMMW Upload started and will continue reading any new data as it's added to the logdir. To stop uploading, press Ctrl-C. New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/CgaWd9pATZeuquRT7TZp7w/ [1m[2023-02-13T11:50:01][0m Started scanning logdir. [1m[2023-02-13T11:50:04][0m Total uploaded: 12630 scalars, 0 tensors, 0 binary objects Interrupted. View your TensorBoard at https://tensorboard.dev/experiment/CgaWd9pATZeuquRT7TZp7w/ Traceback (most recent call last): File "/usr/local/bin/tensorboard", line 8, in <module> sys.exit(run_main()) File "/usr/local/lib/python3.8/dist-packages/tensorboard/main.py", line 46, in run_main app.run(tensorboard.main, flags_parser=tensorboard.configure) File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 308, in run _run_main(main, args) File "/usr/local/lib/python3.8/dist-packages/absl/app.py", line 254, in _run_main sys.exit(main(argv)) File "/usr/local/lib/python3.8/dist-packages/tensorboard/program.py", line 276, in main return runner(self.flags) or 0 File "/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py", line 691, in run return _run(flags, self._experiment_url_callback) File "/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py", line 124, in _run intent.execute(server_info, channel) File "/usr/local/lib/python3.8/dist-packages/grpc/_channel.py", line 1564, in __exit__ self._close() File "/usr/local/lib/python3.8/dist-packages/grpc/_channel.py", line 1550, in _close self._channel.close(cygrpc.StatusCode.cancelled, 'Channel closed!') File "src/python/grpcio/grpc/_cython/_cygrpc/channel.pyx.pxi", line 513, in grpc._cython.cygrpc.Channel.close File "src/python/grpcio/grpc/_cython/_cygrpc/channel.pyx.pxi", line 399, in grpc._cython.cygrpc._close File "src/python/grpcio/grpc/_cython/_cygrpc/channel.pyx.pxi", line 429, in grpc._cython.cygrpc._close File "/usr/lib/python3.8/threading.py", line 364, in notify_all def notify_all(self): KeyboardInterrupt ^C
t5model.model.save_pretrained("./model")
t5model.tokenizer.save_pretrained("./model")
('./model/tokenizer_config.json', './model/special_tokens_map.json', './model/spiece.model', './model/added_tokens.json')
!zip -r /content/model model
adding: model/ (stored 0%) adding: model/tokenizer_config.json (deflated 82%) adding: model/config.json (deflated 62%) adding: model/generation_config.json (deflated 29%) adding: model/pytorch_model.bin (deflated 8%) adding: model/special_tokens_map.json (deflated 86%) adding: model/spiece.model (deflated 48%)
!huggingface-cli login
_| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _| _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_| _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _| _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_| To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens . Token: Add token as git credential? (Y/n) y Token is valid. [1m[31mCannot authenticate through git-credential as no helper is defined on your machine. You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set the 'store' credential helper as default. git config --global credential.helper store Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.[0m Token has not been saved to git credential helper. Your token has been saved to /root/.cache/huggingface/token Login successful
t5model.model.push_to_hub("Zombely/t5-model")
Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]
pytorch_model.bin: 0%| | 0.00/242M [00:00<?, ?B/s]
CommitInfo(commit_url='https://huggingface.co/Zombely/t5-model/commit/caab3e8d5523bc7309774f13f522fd6f06666b29', commit_message='Upload T5ForConditionalGeneration', commit_description='', oid='caab3e8d5523bc7309774f13f522fd6f06666b29', pr_url=None, pr_revision=None, pr_num=None)
t5model.tokenizer.push_to_hub("Zombely/t5-tokenizer")
spiece.model: 0%| | 0.00/792k [00:00<?, ?B/s]
Upload 1 LFS files: 0%| | 0/1 [00:00<?, ?it/s]
CommitInfo(commit_url='https://huggingface.co/Zombely/t5-tokenizer/commit/640998bd3d496cbd5266140fbeff873d07c0158e', commit_message='Upload tokenizer', commit_description='', oid='640998bd3d496cbd5266140fbeff873d07c0158e', pr_url=None, pr_revision=None, pr_num=None)