Instructions to use megrisdal/distilbert-rater with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use megrisdal/distilbert-rater with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="megrisdal/distilbert-rater")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("megrisdal/distilbert-rater") model = AutoModelForSequenceClassification.from_pretrained("megrisdal/distilbert-rater", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from megrisdal/distilbert-rater: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/megrisdal/distilbert-rater/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://megrisdal/distilbert-rater/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/megrisdal/distilbert-rater/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- e2877fc09219bbb48ce97d0af204720424e04566d5679d8bbc48f1e5dfdbb489
- Size of remote file:
- 268 MB
- SHA256:
- fb302a9e00ec4c8011354210a406cf89ba88a993d19429c42829591be3c0aa7c
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