Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use ratish/DBERT_CleanDesc_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ratish/DBERT_CleanDesc_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ratish/DBERT_CleanDesc_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ratish/DBERT_CleanDesc_v2") model = AutoModelForSequenceClassification.from_pretrained("ratish/DBERT_CleanDesc_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: ratish/DBERT_CleanDesc_v2 | |
| 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. --> | |
| # ratish/DBERT_CleanDesc_v2 | |
| This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.0738 | |
| - Validation Loss: 0.6606 | |
| - Train Accuracy: 0.85 | |
| - Epoch: 18 | |
| ## 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': 6180, '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 | | |
| |:----------:|:---------------:|:--------------:|:-----:| | |
| | 2.2247 | 2.0414 | 0.375 | 0 | | |
| | 1.6722 | 1.6034 | 0.575 | 1 | | |
| | 1.2412 | 1.3270 | 0.6 | 2 | | |
| | 0.9495 | 1.0999 | 0.6 | 3 | | |
| | 0.7464 | 0.9892 | 0.65 | 4 | | |
| | 0.6087 | 0.8445 | 0.75 | 5 | | |
| | 0.4628 | 0.8918 | 0.7 | 6 | | |
| | 0.3747 | 0.7971 | 0.775 | 7 | | |
| | 0.3069 | 0.7776 | 0.75 | 8 | | |
| | 0.2492 | 0.6877 | 0.825 | 9 | | |
| | 0.2148 | 0.7085 | 0.8 | 10 | | |
| | 0.1793 | 0.6896 | 0.85 | 11 | | |
| | 0.1598 | 0.7230 | 0.85 | 12 | | |
| | 0.1308 | 0.7365 | 0.85 | 13 | | |
| | 0.1211 | 0.6985 | 0.85 | 14 | | |
| | 0.1023 | 0.6592 | 0.85 | 15 | | |
| | 0.0892 | 0.6621 | 0.85 | 16 | | |
| | 0.0885 | 0.6387 | 0.85 | 17 | | |
| | 0.0738 | 0.6606 | 0.85 | 18 | | |
| ### Framework versions | |
| - Transformers 4.27.4 | |
| - TensorFlow 2.12.0 | |
| - Datasets 2.11.0 | |
| - Tokenizers 0.13.3 | |