Instructions to use deivism/model-tf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deivism/model-tf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="deivism/model-tf")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("deivism/model-tf") model = AutoModelForTokenClassification.from_pretrained("deivism/model-tf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from deivism/model-tf: direct link, hf CLI and curl.
- Browser
- Download file 861 Bytes
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https://huggingface.co/deivism/model-tf/resolve/main/README.md
- Command line
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hf download hf://deivism/model-tf/README.md
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curl -L -o README.md https://huggingface.co/deivism/model-tf/resolve/main/README.md
861 Bytes
metadata
library_name: transformers
tags:
- generated_from_keras_callback
model-index:
- name: model-tf
results: []
model-tf
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 4.44.2
- TensorFlow 2.17.0
- Tokenizers 0.19.1