Instructions to use Joshwabail/aspect_extractor_distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Joshwabail/aspect_extractor_distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Joshwabail/aspect_extractor_distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Joshwabail/aspect_extractor_distilbert") model = AutoModelForTokenClassification.from_pretrained("Joshwabail/aspect_extractor_distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Joshwabail/aspect_extractor_distilbert: direct link, hf CLI and curl.
- Browser
- Download file 261 MB
-
https://huggingface.co/Joshwabail/aspect_extractor_distilbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Joshwabail/aspect_extractor_distilbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Joshwabail/aspect_extractor_distilbert/resolve/main/pytorch_model.bin
261 MB
- Xet hash:
- 5e727182b1800aed2a590788bdc0afbcf640587392989c6fff359c459d405af6
- Size of remote file:
- 261 MB
- SHA256:
- 4ad65aecee1f57d74debc429b6575f6881a64ce596951f3a7dd3241f08ce11dd
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