Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Fryktlos/lets_try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fryktlos/lets_try with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fryktlos/lets_try")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fryktlos/lets_try") model = AutoModelForSequenceClassification.from_pretrained("Fryktlos/lets_try", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Fryktlos/lets_try: direct link, hf CLI and curl.
- Browser
- Download file 1.66 kB
-
https://huggingface.co/Fryktlos/lets_try/resolve/main/README.md
- Command line
-
hf download hf://Fryktlos/lets_try/README.md
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curl -L -o README.md https://huggingface.co/Fryktlos/lets_try/resolve/main/README.md
1.66 kB
metadata
base_model: savasy/bert-base-turkish-sentiment-cased
tags:
- generated_from_keras_callback
model-index:
- name: Fryktlos/lets_try
results: []
Fryktlos/lets_try
This model is a fine-tuned version of savasy/bert-base-turkish-sentiment-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.1508
- Validation Loss: 0.5121
- Train Accuracy: 0.8221
- Epoch: 1
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': True, 'is_legacy_optimizer': False, 'learning_rate': 5e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|---|---|---|---|
| 0.3810 | 0.2831 | 0.8702 | 0 |
| 0.1508 | 0.5121 | 0.8221 | 1 |
Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.0