Text Classification
Transformers
TensorFlow
roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use Zarkit/classificationEsp1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zarkit/classificationEsp1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Zarkit/classificationEsp1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Zarkit/classificationEsp1") model = AutoModelForSequenceClassification.from_pretrained("Zarkit/classificationEsp1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Zarkit/classificationEsp1: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Zarkit/classificationEsp1/resolve/main/tf_model.h5
- Command line
-
hf download hf://Zarkit/classificationEsp1/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Zarkit/classificationEsp1/resolve/main/tf_model.h5
499 MB
- Xet hash:
- d6c132a192040d41cba0b9fdd9f0e29b7585270907600658a0084e979c758188
- Size of remote file:
- 499 MB
- SHA256:
- b12ab057ad92b7d1b9d8051fcb7bef5e606457df02e479019f711fd13e584267
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