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 training_args.bin from Joshwabail/aspect_extractor_distilbert: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/Joshwabail/aspect_extractor_distilbert/resolve/main/training_args.bin
- Command line
-
hf download hf://Joshwabail/aspect_extractor_distilbert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Joshwabail/aspect_extractor_distilbert/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- ef672623d84226f219cbca0331b21741f0e2d40d6f0ba8d9d13a463e511ec46a
- Size of remote file:
- 3.06 kB
- SHA256:
- b936d3a5715835708b299d496b40222ee7624b70ceb5a3d8b8aa59de6f8804ac
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