Instructions to use EMBO/sd-ner-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBO/sd-ner-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EMBO/sd-ner-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EMBO/sd-ner-v2") model = AutoModelForTokenClassification.from_pretrained("EMBO/sd-ner-v2", 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 [`microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract`](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) 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://hu…833cf4f
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added widget after updating to PubMedBERT
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