Instructions to use Rocketknight1/temp_upload_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rocketknight1/temp_upload_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rocketknight1/temp_upload_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rocketknight1/temp_upload_test") model = AutoModelForSequenceClassification.from_pretrained("Rocketknight1/temp_upload_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Rocketknight1/temp_upload_test: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/Rocketknight1/temp_upload_test/resolve/main/README.md
- Command line
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hf download hf://Rocketknight1/temp_upload_test/README.md
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curl -L -o README.md https://huggingface.co/Rocketknight1/temp_upload_test/resolve/main/README.md
1.21 kB
| license: apache-2.0 | |
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: Rocketknight1/temp_upload_test | |
| 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. --> | |
| # Rocketknight1/temp_upload_test | |
| This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Train Loss: 0.6858 | |
| - 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': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} | |
| - training_precision: float32 | |
| ### Training results | |
| | Train Loss | Epoch | | |
| |:----------:|:-----:| | |
| | 0.6858 | 0 | | |
| ### Framework versions | |
| - Transformers 4.24.0.dev0 | |
| - TensorFlow 2.10.0 | |
| - Datasets 2.6.1 | |
| - Tokenizers 0.11.0 | |