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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 [`roberta-large`](https://huggingface.co/roberta-large) 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).

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)!

Files changed (1) hide show
  1. README.md +43 -42
README.md CHANGED
@@ -5,76 +5,77 @@ metrics:
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  - f1
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  - precision
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  - recall
 
 
 
 
 
 
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  model-index:
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  - name: tner/roberta-large-tweetner7-selflabel2020
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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: tner/tweetner7
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  type: tner/tweetner7
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  args: tner/tweetner7
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  metrics:
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- - name: F1 (test_2021)
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- type: f1
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  value: 0.6455908683974932
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- - name: Precision (test_2021)
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- type: precision
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  value: 0.6254336513443192
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- - name: Recall (test_2021)
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- type: recall
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  value: 0.6670906567992599
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- - name: Macro F1 (test_2021)
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- type: f1_macro
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  value: 0.5962839441412403
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- - name: Macro Precision (test_2021)
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- type: precision_macro
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  value: 0.5727192958380657
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- - name: Macro Recall (test_2021)
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- type: recall_macro
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  value: 0.6267698180905158
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- - name: Entity Span F1 (test_2021)
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- type: f1_entity_span
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  value: 0.7846231324492194
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- - name: Entity Span Precision (test_2020)
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- type: precision_entity_span
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  value: 0.7600823937554206
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- - name: Entity Span Recall (test_2021)
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- type: recall_entity_span
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  value: 0.8108014340233607
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- - name: F1 (test_2020)
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- type: f1
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  value: 0.6589874095901421
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- - name: Precision (test_2020)
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- type: precision
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  value: 0.6810631229235881
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- - name: Recall (test_2020)
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- type: recall
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  value: 0.6382978723404256
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- - name: Macro F1 (test_2020)
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- type: f1_macro
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  value: 0.6185133813760935
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- - name: Macro Precision (test_2020)
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- type: precision_macro
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  value: 0.6351153721439261
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- - name: Macro Recall (test_2020)
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- type: recall_macro
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  value: 0.6085669577041991
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- - name: Entity Span F1 (test_2020)
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- type: f1_entity_span
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  value: 0.7670865719646207
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- - name: Entity Span Precision (test_2020)
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- type: precision_entity_span
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  value: 0.7932372505543237
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- - name: Entity Span Recall (test_2020)
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- type: recall_entity_span
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  value: 0.7426050856253243
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-
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- pipeline_tag: token-classification
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- widget:
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- - text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
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- example_title: "NER Example 1"
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  ---
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  # tner/roberta-large-tweetner7-selflabel2020
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  - f1
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  - precision
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  - recall
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+ pipeline_tag: token-classification
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+ widget:
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+ - text: 'Get the all-analog Classic Vinyl Edition of `Takin'' Off` Album from {@herbiehancock@}
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+ via {@bluenoterecords@} link below: {{URL}}'
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+ example_title: NER Example 1
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+ base_model: roberta-large
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  model-index:
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  - name: tner/roberta-large-tweetner7-selflabel2020
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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: tner/tweetner7
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  type: tner/tweetner7
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  args: tner/tweetner7
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  metrics:
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+ - type: f1
 
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  value: 0.6455908683974932
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+ name: F1 (test_2021)
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+ - type: precision
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  value: 0.6254336513443192
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+ name: Precision (test_2021)
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+ - type: recall
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  value: 0.6670906567992599
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+ name: Recall (test_2021)
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+ - type: f1_macro
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  value: 0.5962839441412403
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+ name: Macro F1 (test_2021)
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+ - type: precision_macro
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  value: 0.5727192958380657
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+ name: Macro Precision (test_2021)
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+ - type: recall_macro
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  value: 0.6267698180905158
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+ name: Macro Recall (test_2021)
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+ - type: f1_entity_span
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  value: 0.7846231324492194
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+ name: Entity Span F1 (test_2021)
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+ - type: precision_entity_span
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  value: 0.7600823937554206
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+ name: Entity Span Precision (test_2020)
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+ - type: recall_entity_span
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  value: 0.8108014340233607
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+ name: Entity Span Recall (test_2021)
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+ - type: f1
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  value: 0.6589874095901421
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+ name: F1 (test_2020)
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+ - type: precision
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  value: 0.6810631229235881
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+ name: Precision (test_2020)
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+ - type: recall
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  value: 0.6382978723404256
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+ name: Recall (test_2020)
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+ - type: f1_macro
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  value: 0.6185133813760935
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+ name: Macro F1 (test_2020)
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+ - type: precision_macro
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  value: 0.6351153721439261
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+ name: Macro Precision (test_2020)
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+ - type: recall_macro
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  value: 0.6085669577041991
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+ name: Macro Recall (test_2020)
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+ - type: f1_entity_span
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  value: 0.7670865719646207
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+ name: Entity Span F1 (test_2020)
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+ - type: precision_entity_span
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  value: 0.7932372505543237
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+ name: Entity Span Precision (test_2020)
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+ - type: recall_entity_span
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  value: 0.7426050856253243
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+ name: Entity Span Recall (test_2020)
 
 
 
 
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  ---
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  # tner/roberta-large-tweetner7-selflabel2020
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