Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("W4nkel/microsoftTurkishTrain")
model = AutoModelForSequenceClassification.from_pretrained("W4nkel/microsoftTurkishTrain")Quick Links
W4nkel/microsoftTurkishTrain
This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.9404
- Validation Loss: 0.8487
- Train Accuracy: 0.57
- 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|---|---|---|---|
| 0.9404 | 0.8487 | 0.57 | 0 |
Framework versions
- Transformers 4.25.1
- TensorFlow 2.11.0
- Datasets 2.8.0
- Tokenizers 0.13.2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="W4nkel/microsoftTurkishTrain")