searcher = GridSearcher(
checkpoint_dir='./ckpt_xlmr_naamapadam_ta',
dataset='ai4bharat/naamapadam', # either of `dataset` (huggingface dataset) or `local_dataset` (custom dataset) should be given
dataset_name='ta',
model="xlm-roberta-base", # language model to fine-tune
epoch=10, # the total epoch (`L` in the figure)
epoch_partial=5, # the number of epoch at 1st stage (`M` in the figure)
n_max_config=1, # the number of models to pass to 2nd stage (`K` in the figure)
batch_size=32,
gradient_accumulation_steps=[2],
crf=[True],
lr=[1e-5],
weight_decay=[None],
random_seed=[42],
lr_warmup_step_ratio=[0.1],
max_grad_norm=[10]
)
searcher.train()
INFO:root:INITIALIZE GRID SEARCHER: 1 configs to try
INFO:root:## 1st RUN: Configuration 0/1 ##
INFO:root:hyperparameters
INFO:root: * dataset: ai4bharat/naamapadam
INFO:root: * dataset_split: train
INFO:root: * dataset_name: ta
INFO:root: * local_dataset: None
INFO:root: * model: xlm-roberta-base
INFO:root: * crf: True
INFO:root: * max_length: 128
INFO:root: * epoch: 10
INFO:root: * batch_size: 32
INFO:root: * lr: 1e-05
INFO:root: * random_seed: 42
INFO:root: * gradient_accumulation_steps: 2
INFO:root: * weight_decay: None
INFO:root: * lr_warmup_step_ratio: 0.1
INFO:root: * max_grad_norm: 10
WARNING:datasets.builder:Found cached dataset naamapadam (/root/.cache/huggingface/datasets/ai4bharat___naamapadam/ta/1.0.0/c1b045180d60b208d2468bdad897d04461f08c7137c04a85220697b1bef7df9a)
JSONDecodeError Traceback (most recent call last)
in
16 max_grad_norm=[10]
17 )
---> 18 searcher.train()
8 frames
/usr/lib/python3.9/json/decoder.py in raw_decode(self, s, idx)
353 obj, end = self.scan_once(s, idx)
354 except StopIteration as err:
--> 355 raise JSONDecodeError("Expecting value", s, err.value) from None
356 return obj, end
JSONDecodeError: Expecting value: line 1 column 1 (char 0)