Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Sunthud/model_oo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sunthud/model_oo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sunthud/model_oo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sunthud/model_oo") model = AutoModelForSequenceClassification.from_pretrained("Sunthud/model_oo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Librarian Bot: Add base_model information to model
This pull request aims to enrich the metadata of your model by adding [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) as a `base_model` field, situated in the `YAML` block of your model's `README.md`. How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection. **Why add this?** Enhancing your model's metadata in this way: - **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub. - **Highlights Impact** - It showcases the contributions and influences different models have within the community. For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer). This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback, queries, or nee…0271c43
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