Instructions to use jfforero/a_different_name2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfforero/a_different_name2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jfforero/a_different_name2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jfforero/a_different_name2") model = AutoModelForSequenceClassification.from_pretrained("jfforero/a_different_name2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from jfforero/a_different_name2: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jfforero/a_different_name2/resolve/main/tf_model.h5
- Command line
-
hf download hf://jfforero/a_different_name2/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/jfforero/a_different_name2/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 39e9c4fe86ae26f0c172725176766660eed34a895a9c9f96c291a14007c7fe1a
- Size of remote file:
- 438 MB
- SHA256:
- 0766983187a46829b540d7d35eb42785e85f711c628bd9a418872ae43027738a
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