Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Rocketknight1/temp-colab-upload-test")
model = AutoModelForSequenceClassification.from_pretrained("Rocketknight1/temp-colab-upload-test")Quick Links
Rocketknight1/temp-colab-upload-test
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: 0.5386
- Validation Loss: 0.0000
- Epoch: 0
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': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Epoch |
|---|---|---|
| 0.5386 | 0.0000 | 0 |
Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rocketknight1/temp-colab-upload-test")