Instructions to use Mr-Wick/Roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mr-Wick/Roberta with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="Mr-Wick/Roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Mr-Wick/Roberta") model = AutoModelForQuestionAnswering.from_pretrained("Mr-Wick/Roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Mr-Wick/Roberta: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
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https://huggingface.co/Mr-Wick/Roberta/resolve/main/README.md
- Command line
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hf download hf://Mr-Wick/Roberta/README.md
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curl -L -o README.md https://huggingface.co/Mr-Wick/Roberta/resolve/main/README.md
1.2 kB
metadata
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Roberta
results: []
Roberta
This model is a fine-tuned version of roberta-base 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: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 16476, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
Training results
Framework versions
- Transformers 4.17.0
- TensorFlow 2.8.0
- Datasets 2.0.0
- Tokenizers 0.11.6