# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("Rocketknight1/transformers-qa")
model = AutoModelForQuestionAnswering.from_pretrained("Rocketknight1/transformers-qa")Quick Links
transformers-qa
This model is a fine-tuned version of distilbert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.9300
- Validation Loss: 1.1437
- Epoch: 1
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:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_float16
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 1.5145 | 1.1500 | 0 |
| 0.9300 | 1.1437 | 1 |
Framework versions
- Transformers 4.16.0.dev0
- TensorFlow 2.6.0
- Datasets 1.16.2.dev0
- Tokenizers 0.10.3
- Downloads last month
- 6
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Rocketknight1/transformers-qa")