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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 [`facebook/bart-base`](https://huggingface.co/facebook/bart-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!

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  1. README.md +24 -32
README.md CHANGED
@@ -1,4 +1,6 @@
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  ---
 
 
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  license:
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  - cc-by-nc-sa-4.0
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  - apache-2.0
@@ -15,50 +17,40 @@ widget:
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  example_title: compound-1
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  - text: i can has cheezburger
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  example_title: cheezburger
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- - text: >-
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- so em if we have an now so with fito ringina know how to estimate the tren
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- given the ereafte mylite trend we can also em an estimate is nod s i again
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- tort watfettering an we have estimated the trend an called wot to be called
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- sthat of exty right now we can and look at wy this should not hare a trend i
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- becan we just remove the trend an and we can we now estimate tesees ona
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- effect of them exty
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  example_title: Transcribed Audio Example 2
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- - text: >-
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- My coworker said he used a financial planner to help choose his stocks so he
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- wouldn't loose money.
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  example_title: incorrect word choice (context)
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- - text: >-
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- good so hve on an tadley i'm not able to make it to the exla session on
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- monday this week e which is why i am e recording pre recording an this
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- excelleision and so to day i want e to talk about two things and first of
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- all em i wont em wene give a summary er about ta ohow to remove trents in
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- these nalitives from time series
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  example_title: lowercased audio transcription output
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  - text: Frustrated, the chairs took me forever to set up.
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  example_title: dangling modifier
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  - text: I would like a peice of pie.
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  example_title: miss-spelling
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- - text: >-
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- Which part of Zurich was you going to go hiking in when we were there for
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  the first time together? ! ?
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  example_title: chatbot on Zurich
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- - text: >-
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- Most of the course is about semantic or content of language but there are
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- also interesting topics to be learned from the servicefeatures except
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- statistics in characters in documents. At this point, Elvthos introduces
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- himself as his native English speaker and goes on to say that if you
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- continue to work on social scnce,
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  example_title: social science ASR summary output
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- - text: >-
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- they are somewhat nearby right yes please i'm not sure how the innish is
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- tepen thut mayyouselect one that istatte lo variants in their property e ere
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- interested and anyone basical e may be applyind reaching the browing
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- approach were
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  - medical course audio transcription
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- inference: False
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  pipeline_tag: text2text-generation
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- language:
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- - en
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  ---
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  ---
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+ language:
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+ - en
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  license:
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  - cc-by-nc-sa-4.0
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  - apache-2.0
 
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  example_title: compound-1
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  - text: i can has cheezburger
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  example_title: cheezburger
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+ - text: so em if we have an now so with fito ringina know how to estimate the tren
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+ given the ereafte mylite trend we can also em an estimate is nod s i again tort
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+ watfettering an we have estimated the trend an called wot to be called sthat of
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+ exty right now we can and look at wy this should not hare a trend i becan we just
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+ remove the trend an and we can we now estimate tesees ona effect of them exty
 
 
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  example_title: Transcribed Audio Example 2
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+ - text: My coworker said he used a financial planner to help choose his stocks so
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+ he wouldn't loose money.
 
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  example_title: incorrect word choice (context)
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+ - text: good so hve on an tadley i'm not able to make it to the exla session on monday
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+ this week e which is why i am e recording pre recording an this excelleision and
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+ so to day i want e to talk about two things and first of all em i wont em wene
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+ give a summary er about ta ohow to remove trents in these nalitives from time
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+ series
 
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  example_title: lowercased audio transcription output
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  - text: Frustrated, the chairs took me forever to set up.
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  example_title: dangling modifier
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  - text: I would like a peice of pie.
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  example_title: miss-spelling
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+ - text: Which part of Zurich was you going to go hiking in when we were there for
 
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  the first time together? ! ?
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  example_title: chatbot on Zurich
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+ - text: Most of the course is about semantic or content of language but there are
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+ also interesting topics to be learned from the servicefeatures except statistics
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+ in characters in documents. At this point, Elvthos introduces himself as his native
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+ English speaker and goes on to say that if you continue to work on social scnce,
 
 
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  example_title: social science ASR summary output
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+ - text: they are somewhat nearby right yes please i'm not sure how the innish is tepen
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+ thut mayyouselect one that istatte lo variants in their property e ere interested
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+ and anyone basical e may be applyind reaching the browing approach were
 
 
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  - medical course audio transcription
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+ inference: false
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  pipeline_tag: text2text-generation
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+ base_model: facebook/bart-base
 
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  ---
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