Librarian Bot: Add base_model information to model
Browse filesThis pull request aims to enrich the metadata of your model by adding [`bert-base-cased`](https://huggingface.co/bert-base-cased) 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 need assistance, please don't hesitate to reach out to [@davanstrien](https://huggingface.co/davanstrien). Your input is invaluable to us!
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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- recall
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- f1
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- accuracy
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model-index:
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- name: bert-finetuned-ner-ontonotes
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results:
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- task:
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name: Token Classification
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type: token-classification
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dataset:
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name: ontonotes5
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type: ontonotes5
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split: train
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args: ontonotes5
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metrics:
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-
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type: precision
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value: 0.8567258883248731
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value: 0.8841595180407308
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value: 0.8702265476459025
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value: 0.9754933764288157
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- text: 'Hi! I am jack. I live in California and I work for Apple '
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example_title: Example 1
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- text: 'Thi book is amazing! I bought it on Amazon for 4$. '
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example_title: Example 2
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language:
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- en
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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---
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language:
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- en
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license: apache-2.0
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tags:
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- generated_from_trainer
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- recall
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- f1
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- accuracy
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widget:
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- text: 'Hi! I am jack. I live in California and I work for Apple '
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example_title: Example 1
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- text: 'Thi book is amazing! I bought it on Amazon for 4$. '
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example_title: Example 2
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base_model: bert-base-cased
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model-index:
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- name: bert-finetuned-ner-ontonotes
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results:
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- task:
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type: token-classification
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name: Token Classification
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dataset:
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name: ontonotes5
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type: ontonotes5
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split: train
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args: ontonotes5
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metrics:
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- type: precision
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value: 0.8567258883248731
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name: Precision
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- type: recall
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value: 0.8841595180407308
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name: Recall
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- type: f1
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value: 0.8702265476459025
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name: F1
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- type: accuracy
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value: 0.9754933764288157
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name: Accuracy
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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