Instructions to use aydippy/dippy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aydippy/dippy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aydippy/dippy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aydippy/dippy") model = AutoModelForSequenceClassification.from_pretrained("aydippy/dippy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from aydippy/dippy: direct link, hf CLI and curl.
- Browser
- Download file 2 kB
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https://huggingface.co/aydippy/dippy/resolve/main/README.md
- Command line
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hf download hf://aydippy/dippy/README.md
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curl -L -o README.md https://huggingface.co/aydippy/dippy/resolve/main/README.md
2 kB
metadata
license: apache-2.0
base_model: distilbert-base-uncased-finetuned-sst-2-english
tags:
- generated_from_keras_callback
model-index:
- name: dippy
results: []
dippy
This model is a fine-tuned version of distilbert-base-uncased-finetuned-sst-2-english on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0530
- Train Accuracy: 0.9818
- Validation Loss: 0.3083
- Validation Accuracy: 0.8876
- 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 16838, 'end_learning_rate': 0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|---|---|---|---|---|
| 0.1150 | 0.9609 | 0.3167 | 0.8888 | 0 |
| 0.0530 | 0.9818 | 0.3083 | 0.8876 | 1 |
Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Tokenizers 0.15.0