Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Fryktlos/lets_try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fryktlos/lets_try with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fryktlos/lets_try")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fryktlos/lets_try") model = AutoModelForSequenceClassification.from_pretrained("Fryktlos/lets_try", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Fryktlos/lets_try: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/Fryktlos/lets_try/resolve/main/tf_model.h5
- Command line
-
hf download hf://Fryktlos/lets_try/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Fryktlos/lets_try/resolve/main/tf_model.h5
443 MB
- Xet hash:
- 4bc2b271ba518dde7a99bb981a354432d3d179c8c6b3b7b71d14ad8e27c61c09
- Size of remote file:
- 443 MB
- SHA256:
- 97bb8518ef4d0d548818dfbdbbbf3eab867f4cdd9055d0582e311d82f5e5b9c7
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