Downloads · 30 days
5
29% of all-time downloads
sr_ner_tesla_j355
Tanor/sr_ner_tesla_j355
sr_ner_tesla_j355 is a token classification model from Tanor. Use it when you need labels on individual words, such as names. It is set up for spacy. The card lists the license as cc-by-sa-3.0.
srnerteslaj355 is a spaCy model meticulously fine-tuned for Named Entity Recognition in Serbian language texts. This advanced model incorporates a transformer layer based on Jerteh-355, enhancing its analytical capabi…
All-time downloads
17
Public
Repo size
2.7 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
Other1.4 GB · 52%
At a glance
- Task
- Token Classification
- Library
- spacy
- License
- cc-by-sa-3.0
- Access
- Public
- Created
- Feb 1, 2024
- Updated
- Feb 1, 2024
- SHA
- 1c0297ac
Model card
From the Hugging Face model README
sr_ner_tesla_j355 is a spaCy model meticulously fine-tuned for Named Entity Recognition in Serbian language texts. This advanced model incorporates a transformer layer based on Jerteh-355, enhancing its analytical capabilities. It is proficient in identifying 7 distinct categories of entities: PERS (persons), ROLE (professions), DEMO (demonyms), ORG (organizations), LOC (locations), WORK (artworks), and EVENT (events). Detailed information about these categories is available in the accompanying table. The development of this model has been made possible through the support of the Science Fund of the Republic of Serbia, under grant #7276, for the project 'Text Embeddings - Serbian Language Applications - TESLA'.
| Feature | Description |
|---|---|
| Name | sr_ner_tesla_j355 |
| Version | 1.0.0 |
| spaCy | >=3.7.2,<3.8.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | CC BY-SA 3.0 |
| Author | Milica Ikonić Nešić, Saša Petalinkar, Mihailo Škorić, Ranka Stanković |
Label Scheme
<details> <summary>View label scheme (7 labels for 1 components)</summary>| Component | Labels |
|---|---|
ner | DEMO, EVENT, LOC, ORG, PERS, ROLE, WORK |
Accuracy
| Type | Score |
|---|---|
ENTS_F | 95.40 |
ENTS_P | 95.06 |
ENTS_R | 95.74 |
TRANSFORMER_LOSS | 137907.16 |
NER_LOSS | 265590.02 |
- Task
- Token Classification
- Library
- spacy
- License
- cc-by-sa-3.0
- Languages
- sr
- Created
- Feb 1, 2024
- Updated
- Feb 1, 2024