Instructions to use eleldar/theme-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eleldar/theme-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="eleldar/theme-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eleldar/theme-classification") model = AutoModelForSequenceClassification.from_pretrained("eleldar/theme-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from eleldar/theme-classification: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/eleldar/theme-classification/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://eleldar/theme-classification@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/eleldar/theme-classification/resolve/refs%2Fpr%2F1/flax_model.msgpack
1.63 GB
- Xet hash:
- 53b4a100fd4613deec329f9e7d4615b1a40f87434abff4289845f1feada84ffd
- Size of remote file:
- 1.63 GB
- SHA256:
- 5e07d1ae73ae1c1267fd174a3b21c73b0d77bad288f8ed17fb685f79c419a897
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