Text Classification
Transformers
TensorFlow
distilbert
generated_from_keras_callback
text-embeddings-inference
Instructions to use JavierAnd/cliente360_class_weighted_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JavierAnd/cliente360_class_weighted_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JavierAnd/cliente360_class_weighted_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JavierAnd/cliente360_class_weighted_model") model = AutoModelForSequenceClassification.from_pretrained("JavierAnd/cliente360_class_weighted_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from JavierAnd/cliente360_class_weighted_model: direct link, hf CLI and curl.
- Browser
- Download file 270 MB
-
https://huggingface.co/JavierAnd/cliente360_class_weighted_model/resolve/main/tf_model.h5
- Command line
-
hf download hf://JavierAnd/cliente360_class_weighted_model/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/JavierAnd/cliente360_class_weighted_model/resolve/main/tf_model.h5
270 MB
- Xet hash:
- 2577071ce4a04c442382644d53c5935fd0467f8123a7ef0aebe6aeec1c1b10ad
- Size of remote file:
- 270 MB
- SHA256:
- 32d63d4896825f43b7828537d92d5592504991dd0cf45599d1066c84a8d3dc7f
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