# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("uhiccup/train_dataset")
model = AutoModelForQuestionAnswering.from_pretrained("uhiccup/train_dataset", device_map="auto")Quick Links
train_dataset
This model is a fine-tuned version of klue/bert-base on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 2.21.0
- Tokenizers 0.22.1
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Model tree for uhiccup/train_dataset
Base model
klue/bert-base
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="uhiccup/train_dataset")