Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("matmei/ReactionNecessity")
model = AutoModelForSequenceClassification.from_pretrained("matmei/ReactionNecessity")Quick Links
matmei/ReactionNecessity
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1711
- Validation Loss: 0.4069
- 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1750, '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 | Validation Loss | Epoch |
|---|---|---|
| 0.4596 | 0.3837 | 0 |
| 0.2965 | 0.4022 | 1 |
| 0.1711 | 0.4069 | 2 |
Framework versions
- Transformers 4.36.0
- TensorFlow 2.7.1
- Datasets 2.18.0
- Tokenizers 0.15.0
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Model tree for matmei/ReactionNecessity
Base model
distilbert/distilbert-base-uncased
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="matmei/ReactionNecessity")