Text Classification
Transformers
TensorFlow
ONNX
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use tarekziade/topic_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarekziade/topic_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tarekziade/topic_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tarekziade/topic_classification") model = AutoModelForSequenceClassification.from_pretrained("tarekziade/topic_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from tarekziade/topic_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.95 kB
-
https://huggingface.co/tarekziade/topic_classification/resolve/main/README.md
- Command line
-
hf download hf://tarekziade/topic_classification/README.md
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curl -L -o README.md https://huggingface.co/tarekziade/topic_classification/resolve/main/README.md
1.95 kB
| license: mit | |
| tags: | |
| - generated_from_keras_callback | |
| base_model: microsoft/xtremedistil-l6-h256-uncased | |
| model-index: | |
| - name: topic_classification_04 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information Keras had access to. You should | |
| probably proofread and complete it, then remove this comment. --> | |
| # topic_classification_04 | |
| This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.8325 | |
| - Train Sparse Categorical Accuracy: 0.7237 | |
| - Epoch: 9 | |
| ## 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': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Train Sparse Categorical Accuracy | Epoch | | |
| |:----------:|:---------------------------------:|:-----:| | |
| | 1.0735 | 0.6503 | 0 | | |
| | 0.9742 | 0.6799 | 1 | | |
| | 0.9424 | 0.6900 | 2 | | |
| | 0.9199 | 0.6970 | 3 | | |
| | 0.9016 | 0.7026 | 4 | | |
| | 0.8853 | 0.7073 | 5 | | |
| | 0.8707 | 0.7120 | 6 | | |
| | 0.8578 | 0.7160 | 7 | | |
| | 0.8448 | 0.7199 | 8 | | |
| | 0.8325 | 0.7237 | 9 | | |
| ### Framework versions | |
| - Transformers 4.20.1 | |
| - TensorFlow 2.9.1 | |
| - Datasets 2.3.2 | |
| - Tokenizers 0.12.1 | |