# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("arogyaGurkha/koelectra-base-discriminator-finetuned-squad_kor_v1")
model = AutoModelForQuestionAnswering.from_pretrained("arogyaGurkha/koelectra-base-discriminator-finetuned-squad_kor_v1", device_map="auto")Quick Links
YAML Metadata Error:"model-index[0].results[0].metrics" is required
koelectra-base-discriminator-finetuned-squad_kor_v1
This model is a fine-tuned version of monologg/koelectra-base-discriminator on the squad_kor_v1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.5589
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5774 | 1.0 | 4025 | 0.5589 |
Framework versions
- Transformers 4.10.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
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# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="arogyaGurkha/koelectra-base-discriminator-finetuned-squad_kor_v1")