Instructions to use dilip025/dummy-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dilip025/dummy-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dilip025/dummy-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dilip025/dummy-model") model = AutoModelForMaskedLM.from_pretrained("dilip025/dummy-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from dilip025/dummy-model: direct link, hf CLI and curl.
- Browser
- Download file 938 Bytes
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https://huggingface.co/dilip025/dummy-model/resolve/bd96ce10f26773e3c581ae2e17091cbb72b617bd/README.md
- Command line
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hf download hf://dilip025/dummy-model@bd96ce10f26773e3c581ae2e17091cbb72b617bd/README.md
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curl -L -o README.md https://huggingface.co/dilip025/dummy-model/resolve/bd96ce10f26773e3c581ae2e17091cbb72b617bd/README.md
938 Bytes
metadata
license: mit
base_model: camembert-base
tags:
- generated_from_keras_callback
model-index:
- name: dummy-model
results: []
dummy-model
This model is a fine-tuned version of camembert-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: None
- training_precision: float32
Training results
Framework versions
- Transformers 4.31.0
- TensorFlow 2.12.0
- Tokenizers 0.13.3