Librarian Bot: Add base_model information to model
Browse filesThis pull request aims to enrich the metadata of your model by adding [`microsoft/deberta-base`](https://huggingface.co/microsoft/deberta-base) 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!
@@ -9,29 +9,30 @@ metrics:
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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: deberta-finetuned-ner
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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: conll2003
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type: conll2003
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args: conll2003
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metrics:
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-
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type: precision
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value: 0.9577488309953239
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value: 0.9651632446987546
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value: 0.961441743503772
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value: 0.9907182964622135
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- task:
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type: token-classification
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name: Token Classification
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@@ -41,25 +42,25 @@ model-index:
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config: conll2003
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split: test
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metrics:
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-
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type: accuracy
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value: 0.9108823919384779
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verified: true
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-
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type: precision
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value: 0.9308372971460548
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verified: true
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-
-
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type: recall
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value: 0.9213792387183881
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verified: true
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-
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type: f1
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value: 0.9260841198729938
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verified: true
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-
-
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type: loss
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value: 0.8661637306213379
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verified: true
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---
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- recall
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- f1
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- accuracy
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base_model: microsoft/deberta-base
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model-index:
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- name: deberta-finetuned-ner
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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: conll2003
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type: conll2003
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args: conll2003
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metrics:
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- type: precision
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value: 0.9577488309953239
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+
name: Precision
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+
- type: recall
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value: 0.9651632446987546
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name: Recall
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+
- type: f1
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value: 0.961441743503772
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name: F1
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+
- type: accuracy
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value: 0.9907182964622135
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name: Accuracy
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- task:
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type: token-classification
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name: Token Classification
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config: conll2003
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split: test
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metrics:
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+
- type: accuracy
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value: 0.9108823919384779
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+
name: Accuracy
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verified: true
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- type: precision
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value: 0.9308372971460548
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name: Precision
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verified: true
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+
- type: recall
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value: 0.9213792387183881
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name: Recall
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verified: true
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- type: f1
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value: 0.9260841198729938
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+
name: F1
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verified: true
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- type: loss
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value: 0.8661637306213379
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+
name: loss
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verified: true
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---
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