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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 [`dbmdz/distilbert-base-turkish-cased`](https://huggingface.co/dbmdz/distilbert-base-turkish-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).

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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  1. README.md +7 -6
README.md CHANGED
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  ---
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  language:
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  - tr
 
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  tags:
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  - zero-shot-classification
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  - nli
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  - pytorch
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- pipeline_tag: zero-shot-classification
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- license: apache-2.0
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  datasets:
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  - nli_tr
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  metrics:
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  - accuracy
 
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  widget:
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- - text: "Dolar yükselmeye devam ediyor."
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- candidate_labels: "ekonomi, siyaset, spor"
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- - text: "Senaryo çok saçmaydı, beğendim diyemem."
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- candidate_labels: "olumlu, olumsuz"
 
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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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  - tr
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+ license: apache-2.0
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  tags:
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  - zero-shot-classification
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  - nli
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  - pytorch
 
 
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  datasets:
10
  - nli_tr
11
  metrics:
12
  - accuracy
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+ pipeline_tag: zero-shot-classification
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  widget:
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+ - text: Dolar yükselmeye devam ediyor.
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+ candidate_labels: ekonomi, siyaset, spor
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+ - text: Senaryo çok saçmaydı, beğendim diyemem.
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+ candidate_labels: olumlu, olumsuz
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+ base_model: dbmdz/distilbert-base-turkish-cased
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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