Instructions to use mayardelimi/mBert-darija with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mayardelimi/mBert-darija with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mayardelimi/mBert-darija")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mayardelimi/mBert-darija") model = AutoModelForTokenClassification.from_pretrained("mayardelimi/mBert-darija") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
library_name: transformers
|
| 3 |
+
base_model: google-bert/bert-base-multilingual-cased
|
| 4 |
+
tags:
|
| 5 |
+
- mbert
|
| 6 |
+
- text-segmentation
|
| 7 |
+
- token-classification
|
| 8 |
+
- code-switching
|
| 9 |
+
- pytorch
|
| 10 |
+
language:
|
| 11 |
+
- en
|
| 12 |
+
- ar
|
| 13 |
+
- fr
|
| 14 |
+
- arq
|
| 15 |
+
pipeline_tag: token-classification
|
| 16 |
+
license: apache-2.0
|
| 17 |
+
---
|
| 18 |
+
|
| 19 |
+
# mBERT for Multilingual Text Segmentation
|
| 20 |
+
This model is fine-tuned for segmenting text that switches between English, Arabic, French, and Algerian Arabic.
|