Adding `safetensors` variant of this model

README.md

@@ -10,14 +10,14 @@ tags:
 - fill-mask
 - pilot
 datasets:
-- thejosango/nuha-ajp-dataset
+- thejosango/nuha-dataset
 ---
 
-# nuha-ajp-mlm
+# nuha-jo-mlm
 
 ## Model Summary
 
-`nuha-ajp-mlm` is an Arabic BERT model pre-trained from scratch on Jordanian social media text using masked language modelling (MLM). It serves as the domain-adapted base for the NUHA classifier family — [`nuha-ajp-binary`](https://huggingface.co/thejosango/nuha-ajp-binary) and [`nuha-ajp-trinary`](https://huggingface.co/thejosango/nuha-ajp-trinary) — and was developed as part of a **pilot proof-of-concept** for the NUHA project by the [Jordan Open Source Association (JOSA)](https://josa.ngo).
+`nuha-jo-mlm` is an Arabic BERT model pre-trained from scratch on Jordanian social media text using masked language modelling (MLM). It serves as the domain-adapted base for the NUHA classifier family — [`nuha-jo-binary`](https://huggingface.co/thejosango/nuha-jo-binary) and [`nuha-jo-trinary-full`](https://huggingface.co/thejosango/nuha-jo-trinary-full) — and was developed as part of a **pilot proof-of-concept** for the NUHA project by the [Jordan Open Source Association (JOSA)](https://josa.ngo).
 
 Starting from a fresh vocabulary trained on the NUHA corpus rather than fine-tuning an existing Arabic BERT, the model is adapted to the vocabulary and linguistic patterns of colloquial Jordanian Arabic social media text.
 
@@ -25,12 +25,12 @@ Starting from a fresh vocabulary trained on the NUHA corpus rather than fine-tun
 
 ### Direct Use
 
-`nuha-ajp-mlm` can be used for masked token prediction on Arabic social media text, or as a base model for fine-tuning downstream Arabic NLP tasks — particularly those involving informal Jordanian or Levantine Arabic.
+`nuha-jo-mlm` can be used for masked token prediction on Arabic social media text, or as a base model for fine-tuning downstream Arabic NLP tasks — particularly those involving informal Jordanian or Levantine Arabic.
 
 ```python
 from transformers import pipeline
 
-fill_mask = pipeline("fill-mask", model="thejosango/nuha-ajp-mlm")
+fill_mask = pipeline("fill-mask", model="thejosango/nuha-jo-mlm")
 results = fill_mask("هذه المرأة [MASK] جداً")
 for r in results:
     print(r["token_str"], r["score"])
@@ -38,7 +38,7 @@ for r in results:
 
 ### Downstream Use
 
-The primary intended downstream use is fine-tuning for hate speech and gender-based violence detection using the NUHA dataset, as demonstrated by [`nuha-ajp-binary`](https://huggingface.co/thejosango/nuha-ajp-binary) and [`nuha-ajp-trinary`](https://huggingface.co/thejosango/nuha-ajp-trinary).
+The primary intended downstream use is fine-tuning for hate speech and gender-based violence detection using the NUHA dataset, as demonstrated by [`nuha-jo-binary`](https://huggingface.co/thejosango/nuha-jo-binary) and [`nuha-jo-trinary-full`](https://huggingface.co/thejosango/nuha-jo-trinary-full).
 
 ### Out-of-Scope Use
 
@@ -55,7 +55,7 @@ This model was pre-trained on a relatively small, domain-specific corpus of Jord
 
 ### Training Data
 
-Pre-trained on the text portion of the [`thejosango/nuha-ajp-dataset`](https://huggingface.co/datasets/thejosango/nuha-ajp-dataset) — a corpus of Arabic social media comments collected from Jordanian platforms. The `binary` dataset split was used for pre-training (text only, labels ignored).
+Pre-trained on the text portion of the [`thejosango/nuha-dataset`](https://huggingface.co/datasets/thejosango/nuha-dataset) — a corpus of Arabic social media comments collected from Jordanian platforms. The `binary` dataset split was used for pre-training (text only, labels ignored).
 
 ### Training Procedure
 

model.safetensors

@@ -1,3 +1,3 @@
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