# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("peterandrew987/indo-bert-test")
model = AutoModelForQuestionAnswering.from_pretrained("peterandrew987/indo-bert-test")Quick Links
indo-bert-test
This model is a fine-tuned version of indolem/indobert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.5326
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: 3
- eval_batch_size: 4
- 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 |
|---|---|---|---|
| 3.7334 | 1.0 | 2914 | 3.5991 |
| 3.0449 | 2.0 | 5828 | 3.5326 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3
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Model tree for peterandrew987/indo-bert-test
Base model
indolem/indobert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="peterandrew987/indo-bert-test")