|
--- |
|
language: |
|
- en |
|
- de |
|
license: mit |
|
widget: |
|
- text: fix:lets do a comparsion |
|
example_title: EN 1 |
|
- text: fix:Their going to be here so0n |
|
example_title: EN 2 |
|
- text: fix:das idst ein neuZr test |
|
example_title: DE 1 |
|
- text: >- |
|
fix:ein dransformer isd ein mthode mit der ein compuder eine volge von zeichn |
|
übersetz |
|
example_title: DE 2 |
|
- text: fix:can we mix the languages können wir die sprachen mischen |
|
example_title: EN and DE |
|
metrics: |
|
- cer |
|
pipeline_tag: text2text-generation |
|
--- |
|
|
|
This is an experimental model that should fix your typos and punctuation. |
|
If you like to run your own experiments or train for a different language, take a look at [the code](https://github.com/oliverguhr/spelling). |
|
|
|
|
|
## Model description |
|
|
|
This is a proof of concept *spelling correction model for English and German*. |
|
|
|
## Intended uses & limitations |
|
|
|
This project is work in progress, be aware that the model can produce artefacts. |
|
You can test the model using the pipeline-interface: |
|
|
|
```python |
|
from transformers import pipeline |
|
|
|
fix_spelling_pipeline = pipeline("text2text-generation",model="oliverguhr/spelling-correction-multilingual-base") |
|
def fix_spelling(text, max_length = 256): |
|
return fix_spelling_pipeline("fix:"+text,max_length = max_length) |
|
|
|
print(fix_spelling_pipeline("can we mix the languages können wir die sprachen mischen",max_length=2048)) |
|
``` |