# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Z-Jafari/bert-fa-base-uncased-finetuned-DS_Q_N_C_QA-score_all_gt_0.75")
model = AutoModelForQuestionAnswering.from_pretrained("Z-Jafari/bert-fa-base-uncased-finetuned-DS_Q_N_C_QA-score_all_gt_0.75")Quick Links
bert-fa-base-uncased-finetuned-DS_Q_N_C_QA-score_all_gt_0.75
This model is a fine-tuned version of HooshvareLab/bert-fa-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.2893
- {'exact_match': 70.2, 'f1': 74.91252991808093}
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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.5123 | 1.0 | 1542 | 1.6537 |
| 0.2348 | 2.0 | 3084 | 1.6941 |
| 0.0958 | 3.0 | 4626 | 2.2893 |
Framework versions
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for Z-Jafari/bert-fa-base-uncased-finetuned-DS_Q_N_C_QA-score_all_gt_0.75
Base model
HooshvareLab/bert-fa-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Z-Jafari/bert-fa-base-uncased-finetuned-DS_Q_N_C_QA-score_all_gt_0.75")