# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("anuoluwa/sciq_roberta_model")
model = AutoModelForQuestionAnswering.from_pretrained("anuoluwa/sciq_roberta_model")Quick Links
anuoluwa/sciq_roberta_model
This model is a fine-tuned version of deepset/tinyroberta-squad2 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.4149
- Validation Loss: 0.6330
- Epoch: 2
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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 0.7745 | 0.6224 | 0 |
| 0.5424 | 0.6741 | 1 |
| 0.4149 | 0.6330 | 2 |
Framework versions
- Transformers 4.25.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="anuoluwa/sciq_roberta_model")