Instructions to use hf-internal-testing/tiny-random-rembert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-rembert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-rembert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-rembert") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-rembert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - generated_from_keras_callback | |
| model-index: | |
| - name: tiny-random-rembert | |
| 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. --> | |
| # tiny-random-rembert | |
| This model was trained from scratch on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| ## 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: None | |
| - training_precision: float32 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.18.0.dev0 | |
| - TensorFlow 2.7.0 | |
| - Datasets 1.18.3 | |
| - Tokenizers 0.11.0 | |