Instructions to use ViditRaj/BERT_LATEST_Ads_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ViditRaj/BERT_LATEST_Ads_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ViditRaj/BERT_LATEST_Ads_Classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ViditRaj/BERT_LATEST_Ads_Classifier") model = AutoModelForSequenceClassification.from_pretrained("ViditRaj/BERT_LATEST_Ads_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ViditRaj/BERT_LATEST_Ads_Classifier: direct link, hf CLI and curl.
- Browser
- Download file 1.95 kB
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https://huggingface.co/ViditRaj/BERT_LATEST_Ads_Classifier/resolve/main/README.md
- Command line
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hf download hf://ViditRaj/BERT_LATEST_Ads_Classifier/README.md
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curl -L -o README.md https://huggingface.co/ViditRaj/BERT_LATEST_Ads_Classifier/resolve/main/README.md
1.95 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: ViditRaj/BERT_LATEST_Ads_Classifier | |
| 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. --> | |
| # ViditRaj/BERT_LATEST_Ads_Classifier | |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.0433 | |
| - Validation Loss: 0.2119 | |
| - Train Accuracy: 0.9320 | |
| - Epoch: 4 | |
| ## 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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 465, '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 | | |
| |:----------:|:---------------:|:--------------:|:-----:| | |
| | 0.3301 | 0.2277 | 0.9150 | 0 | | |
| | 0.1607 | 0.1973 | 0.9235 | 1 | | |
| | 0.0958 | 0.1931 | 0.9348 | 2 | | |
| | 0.0592 | 0.2309 | 0.9292 | 3 | | |
| | 0.0433 | 0.2119 | 0.9320 | 4 | | |
| ### Framework versions | |
| - Transformers 4.26.1 | |
| - TensorFlow 2.11.0 | |
| - Datasets 2.10.1 | |
| - Tokenizers 0.13.2 | |