# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("radned/test-trainer")
model = AutoModelForSequenceClassification.from_pretrained("radned/test-trainer")Quick Links
test-trainer
This model is a fine-tuned version of bert-base-uncased on the glue dataset.
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- Transformers 4.27.3
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.2
- Downloads last month
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="radned/test-trainer")