Instructions to use YakovElm/Apache10Classic_MSE_Sample_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/Apache10Classic_MSE_Sample_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/Apache10Classic_MSE_Sample_2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/Apache10Classic_MSE_Sample_2") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/Apache10Classic_MSE_Sample_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from YakovElm/Apache10Classic_MSE_Sample_2: direct link, hf CLI and curl.
- Browser
- Download file 1.8 kB
-
https://huggingface.co/YakovElm/Apache10Classic_MSE_Sample_2/resolve/main/README.md
- Command line
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hf download hf://YakovElm/Apache10Classic_MSE_Sample_2/README.md
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curl -L -o README.md https://huggingface.co/YakovElm/Apache10Classic_MSE_Sample_2/resolve/main/README.md
1.8 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: Apache10Classic_MSE_Sample_2 | |
| 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. --> | |
| # Apache10Classic_MSE_Sample_2 | |
| 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: 0.1147 | |
| - Train Accuracy: 0.9385 | |
| - Validation Loss: 0.2628 | |
| - Validation Accuracy: 0.8611 | |
| - Epoch: 2 | |
| ## 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': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | | |
| |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | |
| | 0.1311 | 0.9309 | 0.2285 | 0.8644 | 0 | | |
| | 0.1205 | 0.9383 | 0.2301 | 0.8644 | 1 | | |
| | 0.1147 | 0.9385 | 0.2628 | 0.8611 | 2 | | |
| ### Framework versions | |
| - Transformers 4.29.2 | |
| - TensorFlow 2.12.0 | |
| - Datasets 2.12.0 | |
| - Tokenizers 0.13.3 | |