Instructions to use lvwerra/distilbert-imdb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lvwerra/distilbert-imdb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lvwerra/distilbert-imdb")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lvwerra/distilbert-imdb") model = AutoModelForSequenceClassification.from_pretrained("lvwerra/distilbert-imdb") - Inference
- Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
This is a pull request to add distilbert-base-uncased as a base_model field to the metadata for your model (defined in the YAML block of your model's README.md).
This information was found in the model card by doing a regular expression match on your model's README.md file.
Adding this information to your models makes it easier to discover the links between different models on the Hugging Face Hub and to see the impact models are having on the community.
You can checkout librarian-bots/base_model_explorer for an example of how this sort of metadata can be used to explore the relationships between models on the Hub.
This PR was made by Librarian Bot. Feel free to get in touch with @davanstrien with feedback or questions.