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Librarian Bot: Add base_model information to model

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This 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!

Files changed (1) hide show
  1. README.md +20 -19
README.md CHANGED
@@ -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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- - name: Precision
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- type: precision
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  value: 0.9577488309953239
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- - name: Recall
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- type: recall
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  value: 0.9651632446987546
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- - name: F1
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- type: f1
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  value: 0.961441743503772
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- - name: Accuracy
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- type: accuracy
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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
@@ -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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- - name: Accuracy
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- type: accuracy
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  value: 0.9108823919384779
 
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  verified: true
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- - name: Precision
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- type: precision
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  value: 0.9308372971460548
 
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  verified: true
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- - name: Recall
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- type: recall
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  value: 0.9213792387183881
 
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  verified: true
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- - name: F1
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- type: f1
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  value: 0.9260841198729938
 
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  verified: true
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- - name: loss
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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
26
+ name: Precision
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+ - type: recall
28
  value: 0.9651632446987546
29
+ name: Recall
30
+ - type: f1
31
  value: 0.961441743503772
32
+ 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:
37
  type: token-classification
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  name: Token Classification
 
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  config: conll2003
43
  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
52
  verified: true
53
+ - 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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