# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Firat/distilbert-base-uncased-finetuned-squad")
model = AutoModelForQuestionAnswering.from_pretrained("Firat/distilbert-base-uncased-finetuned-squad")Quick Links
distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of distilbert-base-uncased on the squad dataset. It achieves the following results on the evaluation set:
- Loss: 1.1460
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2856 | 1.0 | 2767 | 1.1919 |
| 1.012 | 2.0 | 5534 | 1.1332 |
| 0.8512 | 3.0 | 8301 | 1.1460 |
Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 1.18.0
- Tokenizers 0.10.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Firat/distilbert-base-uncased-finetuned-squad")