Instructions to use SRDdev/HingMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/HingMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SRDdev/HingMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SRDdev/HingMaskedLM") model = AutoModelForMaskedLM.from_pretrained("SRDdev/HingMaskedLM") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -22,7 +22,7 @@ tags:
|
|
| 22 |
---
|
| 23 |
|
| 24 |
### HingMaskedLM
|
| 25 |
-
This
|
| 26 |
|
| 27 |
### Dataset
|
| 28 |
Hinglish-Top [Dataset](https://huggingface.co/datasets/WillHeld/hinglish_top) columns
|
|
|
|
| 22 |
---
|
| 23 |
|
| 24 |
### HingMaskedLM
|
| 25 |
+
This model trained for Masked Language Modeling for Hinglish Data.
|
| 26 |
|
| 27 |
### Dataset
|
| 28 |
Hinglish-Top [Dataset](https://huggingface.co/datasets/WillHeld/hinglish_top) columns
|