# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("ahcene-ikram/bert-base-squad")
model = AutoModelForQuestionAnswering.from_pretrained("ahcene-ikram/bert-base-squad")Quick Links
bert-base-uncased-fine-squad
This model is a fine-tuned version of bert-base-uncased on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 1.1842
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: 3
- 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 |
|---|---|---|---|
| 1.0625 | 1.0 | 29508 | 1.0876 |
| 0.7613 | 2.0 | 59016 | 1.1842 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ahcene-ikram/bert-base-squad")