prachuryyaIITG nielsr HF Staff commited on
Commit
46d960d
·
1 Parent(s): 588db8f

Add library_name and improve external project links (#1)

Browse files

- Add library_name and improve external project links (6f7152c7857ccd46eb7ba9252acc479621439948)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (1) hide show
  1. README.md +18 -12
README.md CHANGED
@@ -1,24 +1,31 @@
1
  ---
2
- license: mit
3
- language:
4
- - as
5
  base_model:
6
  - google/muril-large-cased
7
- pipeline_tag: token-classification
8
- tags:
9
- - NER
10
- - Named_Entity_Recognition
11
- pretty_name: CLASSER Assamese MuRIL
12
  datasets:
13
  - prachuryyaIITG/CLASSER
 
 
 
14
  metrics:
15
  - f1
16
  - precision
17
  - recall
 
 
 
 
 
 
18
  ---
19
 
20
  **MuRIL is fine-tuned on Assamese [CLASSER](https://huggingface.co/datasets/prachuryyaIITG/CLASSER) dataset for Fine-grained Named Entity Recognition.**
21
 
 
 
 
 
 
 
22
  The tagset of [MultiCoNER2](https://huggingface.co/datasets/MultiCoNER/multiconer_v2) is a fine-grained tagset. The fine to coarse level mapping of the tags are as follows:
23
 
24
  * Location (LOC) : Facility, OtherLOC, HumanSettlement, Station
@@ -44,8 +51,6 @@ Batch Size: 64 <br>
44
  [Prachuryya Kaushik](https://www.linkedin.com/in/pkabundant/) <br>
45
  [Prof. Ashish Anand](https://www.linkedin.com/in/anandashish/)
46
 
47
- CLASSER is a part of the [AWED-FiNER collection](https://huggingface.co/collections/prachuryyaIITG/awed-finer). Please check: [**Paper**](https://huggingface.co/papers/2601.10161) | [**Agentic Tool**](https://github.com/PrachuryyaKaushik/AWED-FiNER) | [**Interactive Demo**](https://huggingface.co/spaces/prachuryyaIITG/AWED-FiNER)
48
-
49
  ## Sample Usage
50
 
51
  The AWED-FiNER agentic tool can be used to interact with expert models trained using this framework. Below is an example:
@@ -106,8 +111,9 @@ If you use this model, please cite the following papers:
106
 
107
  @inproceedings{fetahu2023multiconer,
108
  title={MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition},
109
- author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Rokhlenko, Oleg and Malmasi, Shervin},
110
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
111
  pages={2027--2051},
112
  year={2023}
113
- }
 
 
1
  ---
 
 
 
2
  base_model:
3
  - google/muril-large-cased
 
 
 
 
 
4
  datasets:
5
  - prachuryyaIITG/CLASSER
6
+ language:
7
+ - as
8
+ license: mit
9
  metrics:
10
  - f1
11
  - precision
12
  - recall
13
+ pipeline_tag: token-classification
14
+ tags:
15
+ - NER
16
+ - Named_Entity_Recognition
17
+ pretty_name: CLASSER Assamese MuRIL
18
+ library_name: transformers
19
  ---
20
 
21
  **MuRIL is fine-tuned on Assamese [CLASSER](https://huggingface.co/datasets/prachuryyaIITG/CLASSER) dataset for Fine-grained Named Entity Recognition.**
22
 
23
+ This model is part of the **AWED-FiNER** project, which provides fine-grained NER solutions across 36 languages.
24
+
25
+ - **Paper:** [AWED-FiNER: Agents, Web applications, and Expert Detectors for Fine-grained Named Entity Recognition across 36 Languages for 6.6 Billion Speakers](https://huggingface.co/papers/2601.10161)
26
+ - **GitHub:** https://github.com/PrachuryyaKaushik/AWED-FiNER
27
+ - **Interactive Demo:** [AWED-FiNER Space](https://huggingface.co/spaces/prachuryyaIITG/AWED-FiNER)
28
+
29
  The tagset of [MultiCoNER2](https://huggingface.co/datasets/MultiCoNER/multiconer_v2) is a fine-grained tagset. The fine to coarse level mapping of the tags are as follows:
30
 
31
  * Location (LOC) : Facility, OtherLOC, HumanSettlement, Station
 
51
  [Prachuryya Kaushik](https://www.linkedin.com/in/pkabundant/) <br>
52
  [Prof. Ashish Anand](https://www.linkedin.com/in/anandashish/)
53
 
 
 
54
  ## Sample Usage
55
 
56
  The AWED-FiNER agentic tool can be used to interact with expert models trained using this framework. Below is an example:
 
111
 
112
  @inproceedings{fetahu2023multiconer,
113
  title={MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition},
114
+ author={Fetahu, Besnik and Chen, Zhiyu and Kar, Sudipta and Oleg and Malmasi, Shervin},
115
  booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
116
  pages={2027--2051},
117
  year={2023}
118
+ }
119
+ ```