Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,25 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
metrics:
|
| 5 |
+
- accuracy
|
| 6 |
+
- f1
|
| 7 |
+
library_name: transformers
|
| 8 |
+
pipeline_tag: token-classification
|
| 9 |
+
tags:
|
| 10 |
+
- deberta-v3
|
| 11 |
+
---
|
| 12 |
+
|
| 13 |
+
## Deberta for Named Entity Recognition
|
| 14 |
+
|
| 15 |
+
I used a Pretrained Deberta-v3-base and finetuned it on Few-NERD, A NER dataset that contains over 180k examples and over 4.6 million tokens.
|
| 16 |
+
|
| 17 |
+
The Token labels are Person, Organisation, Location, Building, Event, Product, Art & Misc.
|
| 18 |
+
|
| 19 |
+
## How to use the model
|
| 20 |
+
|
| 21 |
+
```python
|
| 22 |
+
from transformers import pipeline
|
| 23 |
+
pipe = pipeline(model='RashidNLP/NER-Deberta')
|
| 24 |
+
pipe(["Elon Musk will be at SpaceX's Starbase facility in Boca Chica for the orbital launch of starship next month"])
|
| 25 |
+
```
|