librarian-bot commited on
Commit
d3d5152
1 Parent(s): e5e7f3f

Librarian Bot: Add base_model information to model

Browse files

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 +24 -24
README.md CHANGED
@@ -5,49 +5,49 @@ metrics:
5
  - f1
6
  - precision
7
  - recall
 
 
 
 
 
8
  model-index:
9
  - name: tner/roberta-large-bc5cdr
10
  results:
11
  - task:
12
- name: Token Classification
13
  type: token-classification
 
14
  dataset:
15
  name: tner/bc5cdr
16
  type: tner/bc5cdr
17
  args: tner/bc5cdr
18
  metrics:
19
- - name: F1
20
- type: f1
21
  value: 0.8840696387239609
22
- - name: Precision
23
- type: precision
24
  value: 0.8728266269249876
25
- - name: Recall
26
- type: recall
27
  value: 0.8956060760526048
28
- - name: F1 (macro)
29
- type: f1_macro
30
  value: 0.8797360472482783
31
- - name: Precision (macro)
32
- type: precision_macro
33
  value: 0.8684274142690976
34
- - name: Recall (macro)
35
- type: recall_macro
36
  value: 0.8913672531528037
37
- - name: F1 (entity span)
38
- type: f1_entity_span
39
  value: 0.886283586595552
40
- - name: Precision (entity span)
41
- type: precision_entity_span
42
  value: 0.8750124192747144
43
- - name: Recall (entity span)
44
- type: recall_entity_span
45
  value: 0.8978489142624121
46
-
47
- pipeline_tag: token-classification
48
- widget:
49
- - text: "Jacob Collier is a Grammy awarded artist from England."
50
- example_title: "NER Example 1"
51
  ---
52
  # tner/roberta-large-bc5cdr
53
 
 
5
  - f1
6
  - precision
7
  - recall
8
+ pipeline_tag: token-classification
9
+ widget:
10
+ - text: Jacob Collier is a Grammy awarded artist from England.
11
+ example_title: NER Example 1
12
+ base_model: roberta-large
13
  model-index:
14
  - name: tner/roberta-large-bc5cdr
15
  results:
16
  - task:
 
17
  type: token-classification
18
+ name: Token Classification
19
  dataset:
20
  name: tner/bc5cdr
21
  type: tner/bc5cdr
22
  args: tner/bc5cdr
23
  metrics:
24
+ - type: f1
 
25
  value: 0.8840696387239609
26
+ name: F1
27
+ - type: precision
28
  value: 0.8728266269249876
29
+ name: Precision
30
+ - type: recall
31
  value: 0.8956060760526048
32
+ name: Recall
33
+ - type: f1_macro
34
  value: 0.8797360472482783
35
+ name: F1 (macro)
36
+ - type: precision_macro
37
  value: 0.8684274142690976
38
+ name: Precision (macro)
39
+ - type: recall_macro
40
  value: 0.8913672531528037
41
+ name: Recall (macro)
42
+ - type: f1_entity_span
43
  value: 0.886283586595552
44
+ name: F1 (entity span)
45
+ - type: precision_entity_span
46
  value: 0.8750124192747144
47
+ name: Precision (entity span)
48
+ - type: recall_entity_span
49
  value: 0.8978489142624121
50
+ name: Recall (entity span)
 
 
 
 
51
  ---
52
  # tner/roberta-large-bc5cdr
53