# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("sinu/IndoBERT-ExamQA")
model = AutoModelForQuestionAnswering.from_pretrained("sinu/IndoBERT-ExamQA")Quick Links
IndoBERT-ExamQA
This model is a fine-tuned version of indolem/indobert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8183
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: -
- train_batch_size: -
- eval_batch_size: -
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.395 | 1.0 | 8202 | 1.3536 |
| 1.1534 | 2.0 | 16404 | 1.4040 |
| 1.2816 | 2.0 | 32808 | 1.8183 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="sinu/IndoBERT-ExamQA")