How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("question-answering", model="Mr-Wick/Albert")
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering

tokenizer = AutoTokenizer.from_pretrained("Mr-Wick/Albert")
model = AutoModelForQuestionAnswering.from_pretrained("Mr-Wick/Albert")
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Mr-Wick/Albert

This model is a fine-tuned version of Mr-Wick/Albert on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.4248
  • Train End Logits Accuracy: 0.3423
  • Train Loss Accuracy: 0.0664
  • Train Start Logits Accuracy: 0.3437
  • Validation Loss: 0.9468
  • Validation End Logits Accuracy: 0.4724
  • Validation Loss Accuracy: 0.0591
  • Validation Start Logits Accuracy: 0.4772
  • 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16494, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
  • training_precision: float32

Training results

Train Loss Train End Logits Accuracy Train Loss Accuracy Train Start Logits Accuracy Validation Loss Validation End Logits Accuracy Validation Loss Accuracy Validation Start Logits Accuracy Epoch
0.6581 0.3488 0.0671 0.3529 0.9366 0.4415 0.0657 0.4486 0
0.4248 0.3423 0.0664 0.3437 0.9468 0.4724 0.0591 0.4772 1

Framework versions

  • Transformers 4.17.0
  • TensorFlow 2.8.0
  • Datasets 2.0.0
  • Tokenizers 0.11.6
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