Librarian Bot: Add base_model information to model
Browse filesThis pull request aims to enrich the metadata of your model by adding [`facebook/esm2_t12_35M_UR50D`](https://huggingface.co/facebook/esm2_t12_35M_UR50D) as a `base_model` field, situated in the `YAML` block of your model's `README.md`.
How did we find this information? We extracted this infromation from the `adapter_config.json` file of your model.
**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 need assistance, please don't hesitate to reach out to [@davanstrien](https://huggingface.co/davanstrien).
If you want to automatically add `base_model` metadata to more of your modes you can use the [Librarian Bot](https://huggingface.co/librarian-bot) [Metadata Request Service](https://huggingface.co/spaces/librarian-bots/metadata_request_service)!
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---
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license: mit
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datasets:
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- AmelieSchreiber/binding_sites_random_split_by_family_550K
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metrics:
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- precision
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- recall
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- matthews_correlation
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language:
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- en
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tags:
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- ESM-2
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- protein language model
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- binding sites
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- biology
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pipeline_tag: token-classification
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---
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# ESM-2 for Binding Site Prediction
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---
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language:
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- en
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license: mit
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library_name: peft
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tags:
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- ESM-2
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- protein language model
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- binding sites
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- biology
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datasets:
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- AmelieSchreiber/binding_sites_random_split_by_family_550K
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metrics:
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- precision
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- recall
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- matthews_correlation
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pipeline_tag: token-classification
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base_model: facebook/esm2_t12_35M_UR50D
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---
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# ESM-2 for Binding Site Prediction
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