# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tai-dang11/test")
model = AutoModelForSequenceClassification.from_pretrained("tai-dang11/test")Quick Links
test
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
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': 7810, '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
Framework versions
- Transformers 4.16.0.dev0
- TensorFlow 2.7.0
- Datasets 1.17.0
- Tokenizers 0.10.3
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tai-dang11/test")