FiqhQA
Collection
27 items • Updated
How to use mhdafifan/xlm-roberta-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/xlm-roberta-twostage-qasina-fiqhqa-ML-384") # Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("mhdafifan/xlm-roberta-twostage-qasina-fiqhqa-ML-384")
model = AutoModelForQuestionAnswering.from_pretrained("mhdafifan/xlm-roberta-twostage-qasina-fiqhqa-ML-384")This model is a fine-tuned version of mhdafifan/xlm-roberta-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 |
|---|---|---|---|---|---|
| 1.553 | 1.0 | 204 | 1.2515 | 48.35 | 22.33 |
| 1.2664 | 2.0 | 408 | 1.0844 | 48.67 | 26.21 |
| 1.0276 | 3.0 | 612 | 1.1402 | 48.02 | 27.18 |
| 0.8913 | 4.0 | 816 | 1.0797 | 51.17 | 29.13 |
| 0.8443 | 5.0 | 1020 | 1.1835 | 50.49 | 28.16 |
Base model
FacebookAI/xlm-roberta-base