Instructions to use christopheparisse/complexity_80_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use christopheparisse/complexity_80_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="christopheparisse/complexity_80_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("christopheparisse/complexity_80_model") model = AutoModelForSequenceClassification.from_pretrained("christopheparisse/complexity_80_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from christopheparisse/complexity_80_model: direct link, hf CLI and curl.
- Browser
- Download file 874 Bytes
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https://huggingface.co/christopheparisse/complexity_80_model/resolve/main/README.md
- Command line
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hf download hf://christopheparisse/complexity_80_model/README.md
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curl -L -o README.md https://huggingface.co/christopheparisse/complexity_80_model/resolve/main/README.md
874 Bytes
metadata
tags:
- generated_from_keras_callback
model-index:
- name: complexity_80_model
results: []
complexity_80_model
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 4.36.0
- TensorFlow 2.13.1
- Datasets 2.15.0
- Tokenizers 0.15.0