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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 [`google/flan-t5-xl`](https://huggingface.co/google/flan-t5-xl) 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 +40 -27
README.md CHANGED
@@ -1,7 +1,5 @@
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  ---
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- languages:
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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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  tags:
@@ -11,32 +9,46 @@ tags:
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  - error-correction
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  - grammar synthesis
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  - FLAN
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-
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  datasets:
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  - jfleg
 
 
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  widget:
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-
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- - text: "There car broke down so their hitching a ride to they're class."
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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 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"
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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 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 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"
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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: "simple miss-spelling"
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- - text: "Which part of Zurich was you going to go hiking in when we were there for 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 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,"
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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 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"
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- - example_title: "medical course audio transcription"
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-
 
 
 
 
 
 
 
 
 
 
 
 
 
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  inference:
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  parameters:
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  max_length: 96
@@ -44,7 +56,8 @@ inference:
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  num_beams: 2
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  repetition_penalty: 1.15
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  length_penalty: 1
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- early_stopping: True
 
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  ---
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  # grammar-synthesis: flan-t5-xl
 
1
  ---
2
+ license:
 
 
3
  - cc-by-nc-sa-4.0
4
  - apache-2.0
5
  tags:
 
9
  - error-correction
10
  - grammar synthesis
11
  - FLAN
 
12
  datasets:
13
  - jfleg
14
+ languages:
15
+ - en
16
  widget:
17
+ - text: There car broke down so their hitching a ride to they're class.
18
+ example_title: compound-1
19
+ - text: i can has cheezburger
20
+ example_title: cheezburger
21
+ - text: so em if we have an now so with fito ringina know how to estimate the tren
22
+ given the ereafte mylite trend we can also em an estimate is nod s i again tort
23
+ watfettering an we have estimated the trend an called wot to be called sthat of
24
+ exty right now we can and look at wy this should not hare a trend i becan we just
25
+ remove the trend an and we can we now estimate tesees ona effect of them exty
26
+ example_title: Transcribed Audio Example 2
27
+ - text: My coworker said he used a financial planner to help choose his stocks so
28
+ he wouldn't loose money.
29
+ example_title: incorrect word choice (context)
30
+ - text: good so hve on an tadley i'm not able to make it to the exla session on monday
31
+ this week e which is why i am e recording pre recording an this excelleision and
32
+ so to day i want e to talk about two things and first of all em i wont em wene
33
+ give a summary er about ta ohow to remove trents in these nalitives from time
34
+ series
35
+ example_title: lowercased audio transcription output
36
+ - text: Frustrated, the chairs took me forever to set up.
37
+ example_title: dangling modifier
38
+ - text: I would like a peice of pie.
39
+ example_title: simple miss-spelling
40
+ - text: Which part of Zurich was you going to go hiking in when we were there for
41
+ the first time together? ! ?
42
+ example_title: chatbot on Zurich
43
+ - text: Most of the course is about semantic or content of language but there are
44
+ also interesting topics to be learned from the servicefeatures except statistics
45
+ in characters in documents. At this point, Elvthos introduces himself as his native
46
+ English speaker and goes on to say that if you continue to work on social scnce,
47
+ example_title: social science ASR summary output
48
+ - text: they are somewhat nearby right yes please i'm not sure how the innish is tepen
49
+ thut mayyouselect one that istatte lo variants in their property e ere interested
50
+ and anyone basical e may be applyind reaching the browing approach were
51
+ - example_title: medical course audio transcription
52
  inference:
53
  parameters:
54
  max_length: 96
 
56
  num_beams: 2
57
  repetition_penalty: 1.15
58
  length_penalty: 1
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+ early_stopping: true
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+ base_model: google/flan-t5-xl
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  ---
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  # grammar-synthesis: flan-t5-xl