Librarian Bot: Add base_model information to model

This pull request aims to enrich the metadata of your model by adding [`google/t5-small-lm-adapt`](https://huggingface.co/google/t5-small-lm-adapt) 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!

README.md

@@ -1,7 +1,5 @@
 ---
-languages:
-- en
-license: 
+license:
 - cc-by-nc-sa-4.0
 - apache-2.0
 tags:
@@ -11,45 +9,54 @@ tags:
 - error-correction
 - grammar synthesis
 - FLAN
-
 datasets:
 - jfleg
+languages:
+- en
 widget:
-
-- text: "There car broke down so their hitching a ride to they're class."
-  example_title: "compound-1"
-- text: "i can has cheezburger"
-  example_title: "cheezburger"
-- text: "so em if we have an now so with fito ringina know how to estimate the tren given the ereafte mylite trend we can also em an estimate is nod s
-i again tort watfettering an we have estimated the trend an
-called wot to be called sthat of exty right now we can and look at
-wy this should not hare a trend i becan we just remove the trend an and we can we now estimate
-tesees ona effect of them exty"
-  example_title: "Transcribed Audio Example 2"
-- text: "My coworker said he used a financial planner to help choose his stocks so he wouldn't loose money."
-  example_title: "incorrect word choice (context)"
-- text: "good so hve on an tadley i'm not able to make it to the exla session on monday this week e which is why i am e recording pre recording
-an this excelleision and so to day i want e to talk about two things and first of all em i wont em wene give a summary er about
-ta ohow to remove trents in these nalitives from time series"
-  example_title: "lowercased audio transcription output"
-- text: "Frustrated, the chairs took me forever to set up."
-  example_title: "dangling modifier"
-- text: "I would like a peice of pie."
-  example_title: "miss-spelling"
-- text: "Which part of Zurich was you going to go hiking in when we were there for the first time together? ! ?"
-  example_title: "chatbot on Zurich"
-- text: "Most of the course is about semantic or  content of language but there are also interesting topics to be learned from the servicefeatures except statistics in characters in documents. At this point, Elvthos introduces himself as his native English speaker and goes on to say that if you continue to work on social scnce,"
-  example_title: "social science ASR summary output"
-- text: "they are somewhat nearby right yes please i'm not sure how the innish is tepen thut mayyouselect one that istatte lo variants in their property e ere interested and anyone basical e may be applyind reaching the browing approach were"
-- "medical course audio transcription"
-
+- text: There car broke down so their hitching a ride to they're class.
+  example_title: compound-1
+- text: i can has cheezburger
+  example_title: cheezburger
+- text: so em if we have an now so with fito ringina know how to estimate the tren
+    given the ereafte mylite trend we can also em an estimate is nod s i again tort
+    watfettering an we have estimated the trend an called wot to be called sthat of
+    exty right now we can and look at wy this should not hare a trend i becan we just
+    remove the trend an and we can we now estimate tesees ona effect of them exty
+  example_title: Transcribed Audio Example 2
+- text: My coworker said he used a financial planner to help choose his stocks so
+    he wouldn't loose money.
+  example_title: incorrect word choice (context)
+- text: good so hve on an tadley i'm not able to make it to the exla session on monday
+    this week e which is why i am e recording pre recording an this excelleision and
+    so to day i want e to talk about two things and first of all em i wont em wene
+    give a summary er about ta ohow to remove trents in these nalitives from time
+    series
+  example_title: lowercased audio transcription output
+- text: Frustrated, the chairs took me forever to set up.
+  example_title: dangling modifier
+- text: I would like a peice of pie.
+  example_title: miss-spelling
+- text: Which part of Zurich was you going to go hiking in when we were there for
+    the first time together? ! ?
+  example_title: chatbot on Zurich
+- text: Most of the course is about semantic or  content of language but there are
+    also interesting topics to be learned from the servicefeatures except statistics
+    in characters in documents. At this point, Elvthos introduces himself as his native
+    English speaker and goes on to say that if you continue to work on social scnce,
+  example_title: social science ASR summary output
+- text: they are somewhat nearby right yes please i'm not sure how the innish is tepen
+    thut mayyouselect one that istatte lo variants in their property e ere interested
+    and anyone basical e may be applyind reaching the browing approach were
+- medical course audio transcription
 parameters:
   max_length: 128
   min_length: 4
   num_beams: 8
   repetition_penalty: 1.21
   length_penalty: 1
-  early_stopping: True
+  early_stopping: true
+base_model: google/t5-small-lm-adapt
 ---
 
 # grammar-synthesis-small (beta)