Update README.md
Browse files
README.md
CHANGED
|
@@ -5,9 +5,16 @@ language:
|
|
| 5 |
library_name: gliner
|
| 6 |
datasets:
|
| 7 |
- urchade/pile-mistral-v0.1
|
|
|
|
|
|
|
| 8 |
pipeline_tag: token-classification
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 9 |
---
|
| 10 |
-
|
| 11 |
# About
|
| 12 |
|
| 13 |
GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
|
|
|
|
| 5 |
library_name: gliner
|
| 6 |
datasets:
|
| 7 |
- urchade/pile-mistral-v0.1
|
| 8 |
+
- numind/NuNER
|
| 9 |
+
- knowledgator/GLINER-multi-task-synthetic-data
|
| 10 |
pipeline_tag: token-classification
|
| 11 |
+
tags:
|
| 12 |
+
- NER
|
| 13 |
+
- GLiNER
|
| 14 |
+
- information extraction
|
| 15 |
+
- encoder
|
| 16 |
+
- entity recognition
|
| 17 |
---
|
|
|
|
| 18 |
# About
|
| 19 |
|
| 20 |
GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
|