Instructions to use RJ3vans/SignTagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RJ3vans/SignTagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RJ3vans/SignTagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RJ3vans/SignTagger") model = AutoModelForTokenClassification.from_pretrained("RJ3vans/SignTagger") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -10,7 +10,9 @@ pipeline_tag: token-classification
|
|
| 10 |
tags:
|
| 11 |
- code
|
| 12 |
---
|
| 13 |
-
This model is used to tag the tokens in an input sequence with information about the different signs of
|
|
|
|
|
|
|
| 14 |
|
| 15 |
It was derived using code written by Dr. Le An Ha at the University of Wolverhampton.
|
| 16 |
|
|
|
|
| 10 |
tags:
|
| 11 |
- code
|
| 12 |
---
|
| 13 |
+
This model is used to tag the tokens in an input sequence with information about the different signs of
|
| 14 |
+
syntactic complexity that they contain. For more details, please see Chapters 2 and 3 of my thesis
|
| 15 |
+
(https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf.
|
| 16 |
|
| 17 |
It was derived using code written by Dr. Le An Ha at the University of Wolverhampton.
|
| 18 |
|