Resume2
Sat 17 May 2025
# https://github.com/urchade/GLiNER/blob/main/examples/finetune.ipynb
!pip show accelerate
Name: accelerate
Version: 1.1.1
Summary: Accelerate
Home-page: https://github.com/huggingface/accelerate
Author: The HuggingFace team
Author-email: zach.mueller@huggingface.co
License: Apache
Location: /home/rajaraman/miniconda3/envs/ml312/lib/python3.12/site-packages
Requires: huggingface-hub, numpy, packaging, psutil, pyyaml, safetensors, torch
Required-by: peft
!wget -P ~/datasets/. https://huggingface.co/datasets/urchade/synthetic-pii-ner-mistral-v1/resolve/main/data.json
--2024-11-20 15:14:13-- https://huggingface.co/datasets/urchade/synthetic-pii-ner-mistral-v1/resolve/main/data.json
Resolving huggingface.co (huggingface.co)... 54.230.27.69, 54.230.27.75, 54.230.27.119, ...
Connecting to huggingface.co (huggingface.co)|54.230.27.69|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://cdn-lfs-us-1.hf.co/repos/57/a8/57a8ec4f4ec288845876da262e329709a3abab15f5604211687806ff1c31f16f/78680af45ca6b1175c502db2eec441933de7383f440fc0f0bae46a72be13c0c7?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27data.json%3B+filename%3D%22data.json%22%3B&response-content-type=application%2Fjson&Expires=1732355053&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczMjM1NTA1M319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzU3L2E4LzU3YThlYzRmNGVjMjg4ODQ1ODc2ZGEyNjJlMzI5NzA5YTNhYmFiMTVmNTYwNDIxMTY4NzgwNmZmMWMzMWYxNmYvNzg2ODBhZjQ1Y2E2YjExNzVjNTAyZGIyZWVjNDQxOTMzZGU3MzgzZjQ0MGZjMGYwYmFlNDZhNzJiZTEzYzBjNz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=E5rpSeFAbhWY88-sU9Q9Q5XrWFwJEZqlORGLShqEAjo3PNPECF9NNAUiIzW7xaIfnbmof9V8wBjb3p3VR5bGubmZ6j0s2R1-QiiNPACr0SCm6twXLnykhGKAjovku1U4yUjqqBuXRUTZ5irqdVxGHC%7EzhLAmhTGe2w422c1atWiJw9vNTwLymbenetBA%7Egott-agYiRJgboV-qG5P3oyBFK0CzKwS5mbK0HVRSXw4-SI4vCXipiUrbYNU29-5Oktu2nzlXuRMip390v274wWWTMYY1sjUW6VZnXnzvIgR%7ERQH-jbtUd7YgXYsDJsgMtwfUlpT1QXYBDH7BDN%7ENXv0g__&Key-Pair-Id=K24J24Z295AEI9 [following]
--2024-11-20 15:14:13-- https://cdn-lfs-us-1.hf.co/repos/57/a8/57a8ec4f4ec288845876da262e329709a3abab15f5604211687806ff1c31f16f/78680af45ca6b1175c502db2eec441933de7383f440fc0f0bae46a72be13c0c7?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27data.json%3B+filename%3D%22data.json%22%3B&response-content-type=application%2Fjson&Expires=1732355053&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTczMjM1NTA1M319LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzU3L2E4LzU3YThlYzRmNGVjMjg4ODQ1ODc2ZGEyNjJlMzI5NzA5YTNhYmFiMTVmNTYwNDIxMTY4NzgwNmZmMWMzMWYxNmYvNzg2ODBhZjQ1Y2E2YjExNzVjNTAyZGIyZWVjNDQxOTMzZGU3MzgzZjQ0MGZjMGYwYmFlNDZhNzJiZTEzYzBjNz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=E5rpSeFAbhWY88-sU9Q9Q5XrWFwJEZqlORGLShqEAjo3PNPECF9NNAUiIzW7xaIfnbmof9V8wBjb3p3VR5bGubmZ6j0s2R1-QiiNPACr0SCm6twXLnykhGKAjovku1U4yUjqqBuXRUTZ5irqdVxGHC%7EzhLAmhTGe2w422c1atWiJw9vNTwLymbenetBA%7Egott-agYiRJgboV-qG5P3oyBFK0CzKwS5mbK0HVRSXw4-SI4vCXipiUrbYNU29-5Oktu2nzlXuRMip390v274wWWTMYY1sjUW6VZnXnzvIgR%7ERQH-jbtUd7YgXYsDJsgMtwfUlpT1QXYBDH7BDN%7ENXv0g__&Key-Pair-Id=K24J24Z295AEI9
Resolving cdn-lfs-us-1.hf.co (cdn-lfs-us-1.hf.co)... 18.172.64.104, 18.172.64.30, 18.172.64.13, ...
Connecting to cdn-lfs-us-1.hf.co (cdn-lfs-us-1.hf.co)|18.172.64.104|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 23300735 (22M) [application/json]
Saving to: ‘/home/rajaraman/datasets/./data.json’
data.json 100%[===================>] 22.22M 11.5MB/s in 1.9s
2024-11-20 15:14:16 (11.5 MB/s) - ‘/home/rajaraman/datasets/./data.json’ saved [23300735/23300735]
!ls ~/datasets/gliner-data.json
/home/rajaraman/datasets/gliner-data.json
# !source ~/.zshrc
import json
import random
train_path = "/home/rajaraman/datasets/gliner-data.json"
with open(train_path, "r") as f:
data = json.load(f)
print('Dataset size:', len(data))
random.shuffle(data)
print('Dataset is shuffled...')
train_dataset = data[:int(len(data)*0.9)]
test_dataset = data[int(len(data)*0.9):]
print('Dataset is splitted...')
Dataset size: 19635
Dataset is shuffled...
Dataset is splitted...
!pip show keras
Name: keras
Version: 3.6.0
Summary: Multi-backend Keras.
Home-page: https://github.com/keras-team/keras
Author: Keras team
Author-email: keras-users@googlegroups.com
License: Apache License 2.0
Location: /home/rajaraman/miniconda3/envs/ml312/lib/python3.12/site-packages
Requires: absl-py, h5py, ml-dtypes, namex, numpy, optree, packaging, rich
Required-by: tensorflow
# !pip install keras
#!pip install tf-keras
!pip show tf-keras
Name: tf_keras
Version: 2.18.0
Summary: Deep learning for humans.
Home-page: https://keras.io/
Author: Keras team
Author-email: keras-users@googlegroups.com
License: Apache 2.0
Location: /home/rajaraman/miniconda3/envs/ml312/lib/python3.12/site-packages
Requires: tensorflow
Required-by:
import os
os.environ["TOKENIZERS_PARALLELISM"] = "true"
import torch
from gliner import GLiNERConfig, GLiNER
from gliner.training import Trainer, TrainingArguments
from gliner.data_processing.collator import DataCollatorWithPadding, DataCollator
from gliner.utils import load_config_as_namespace
from gliner.data_processing import WordsSplitter, GLiNERDataset
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
model = GLiNER.from_pretrained("urchade/gliner_small")
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/home/rajaraman/miniconda3/envs/ml312/lib/python3.12/site-packages/transformers/convert_slow_tokenizer.py:562: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.
warnings.warn(
# use it for better performance, it mimics original implementation but it's less memory efficient
data_collator = DataCollator(model.config, data_processor=model.data_processor, prepare_labels=True)
# Optional: compile model for faster training
model.to(device)
print("done")
done
# calculate number of epochs
num_steps = 500
batch_size = 8
data_size = len(train_dataset)
num_batches = data_size // batch_size
num_epochs = max(1, num_steps // num_batches)
training_args = TrainingArguments(
output_dir="models",
learning_rate=5e-6,
weight_decay=0.01,
others_lr=1e-5,
others_weight_decay=0.01,
lr_scheduler_type="linear", #cosine
warmup_ratio=0.1,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
focal_loss_alpha=0.75,
focal_loss_gamma=2,
num_train_epochs=num_epochs,
evaluation_strategy="steps",
save_steps = 100,
save_total_limit=10,
dataloader_num_workers = 0,
use_cpu = False,
report_to="none",
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=test_dataset,
tokenizer=model.data_processor.transformer_tokenizer,
data_collator=data_collator,
)
# trainer.train()
/home/rajaraman/miniconda3/envs/ml312/lib/python3.12/site-packages/transformers/training_args.py:1494: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
warnings.warn(
Score: 15
Category: resume-analysis