FiqhQA
Collection
27 items • Updated
How to use mhdafifan/indobert-twostage-qasina-fiqhqa-ML-384 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("question-answering", model="mhdafifan/indobert-twostage-qasina-fiqhqa-ML-384") # Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("mhdafifan/indobert-twostage-qasina-fiqhqa-ML-384")
model = AutoModelForQuestionAnswering.from_pretrained("mhdafifan/indobert-twostage-qasina-fiqhqa-ML-384")This model is a fine-tuned version of mhdafifan/indobert-qasina-LR-2e-05-BS-8-ML-384 on the FiqhQA dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | F1 | Exact Match |
|---|---|---|---|---|---|
| 2.0255 | 1.0 | 176 | 1.8733 | 27.49 | 2.91 |
| 1.7697 | 2.0 | 352 | 1.8495 | 33.59 | 6.8 |
| 1.6715 | 3.0 | 528 | 1.8934 | 33.94 | 8.74 |
| 1.6375 | 4.0 | 704 | 1.9156 | 34.12 | 8.74 |
| 1.4068 | 5.0 | 880 | 1.9823 | 30.03 | 5.83 |
Base model
indobenchmark/indobert-base-p1