Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use alimazhar-110/website_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alimazhar-110/website_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="alimazhar-110/website_classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("alimazhar-110/website_classification") model = AutoModelForSequenceClassification.from_pretrained("alimazhar-110/website_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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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](https://huggingface.co/spaces/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](https://huggingface.co/davanstrien) with feedback or questions.529fdd1
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