Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use zinoubm/e-comerce-category-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zinoubm/e-comerce-category-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zinoubm/e-comerce-category-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zinoubm/e-comerce-category-classification") model = AutoModelForSequenceClassification.from_pretrained("zinoubm/e-comerce-category-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from zinoubm/e-comerce-category-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.38 kB
-
https://huggingface.co/zinoubm/e-comerce-category-classification/resolve/main/README.md
- Command line
-
hf download hf://zinoubm/e-comerce-category-classification/README.md
-
curl -L -o README.md https://huggingface.co/zinoubm/e-comerce-category-classification/resolve/main/README.md
1.38 kB
metadata
tags:
- generated_from_keras_callback
model-index:
- name: zinoubm/e-comerce-category-classification
results: []
zinoubm/e-comerce-category-classification
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.0481
- Validation Loss: 0.0434
- 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11030, '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 |
|---|---|---|
| 0.0481 | 0.0434 | 0 |
Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2