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View Code? Open in Web Editor NEW[ICLR 2024 spotlight] Making Pre-trained Language Models Great on Tabular Prediction
License: MIT License
[ICLR 2024 spotlight] Making Pre-trained Language Models Great on Tabular Prediction
License: MIT License
Hi, everyone:
I run project, cannot find 'feature_names.json' file.
D:\conda_env\py38_tpberta\python.exe E:\project\tp-berta\scripts\finetune\default\run_default_config_tree.py --task=regression
load data config from: E:\project\tp-berta\scripts\finetune\default\checkpoints\tp-bin
Traceback (most recent call last):
File "E:\project\tp-berta\scripts\finetune\default\run_default_config_tree.py", line 85, in
data_config = DataConfig.from_pretrained(
File "E:\project\tp-berta\lib\data_utils.py", line 93, in from_pretrained
return DataConfig.from_config(config, tokenizer)
File "E:\project\tp-berta\lib\data_utils.py", line 116, in from_config
with open(data_dir /feature_map_file, 'r') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'E:\project\tp-berta\scripts\finetune\default\data\finetune-reg\feature_names.json'
Hello everyone,
I am trying to fine tune tp-berta on the tabfact dataset from hugging face. So far, I have:
But when I run:
python scripts/finetune/default/run_default_config_tpberta.py --dataset "tab_fact" --task "binclass"
I get this assertionError:
load data config from: /content/tp-berta/checkpoints/tp-bin
prepare datasets #(1/1) [tab_fact]Using cached features: lm/cache__tab_fact__0__quantile__None__None__None__None__default__e8f00a71fbb1bc9a606001064edbb78c.pickle
100% 1/1 [00:02<00:00, 2.49s/it]
Token indices sequence length is longer than the specified maximum sequence length for this model (726 > 512). Running this sequence through the model will result in indexing errors
Some weights of the model checkpoint at /content/tp-berta/checkpoints/tp-bin/pytorch_models/best were not used when initializing TPBertaForClassification: ['heads.heads.76.dense.weight', 'heads.heads.44.dense.bias', ...]
I have tried to manually fix the data_loader but so far no luck. Any ideas?
I've fine-tuned on my dataset. How do I predict to get the binary results? I don't find the file.
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