Update README.md

README.md

@@ -17,6 +17,51 @@ This model was introduced in our ACL 2025 paper, [Enhancing Text Editing for Gra
 The model was fine-tuned to fix punctuation (i.e., Pnx) errors. Details about the training procedure, data preprocessing, and hyperparameters are available in the paper.
 The fine-tuning code and associated resources are publicly available on our GitHub repository: https://github.com/CAMeL-Lab/text-editing.
 
+## Intended uses
+To use the `CAMeL-Lab/text-editing-qalb14-pnx` model, you must clone our text editing [GitHub repository](https://github.com/CAMeL-Lab/text-editing) and follow the installation requirements.
+We used this SWEET<sub>Pnx</sub> model to report results on the QALB-2014 dev and test sets in our [paper](https://arxiv.org/abs/2503.00985).
+This model is intended to be used with SWEET<sub>NoPnx</sub> ([`CAMeL-Lab/text-editing-qalb14-nopnx`](https://huggingface.co/CAMeL-Lab/text-editing-qalb14-nopnx)) model.
+
+## How to use
+Clone our text editing [GitHub repository](https://github.com/CAMeL-Lab/text-editing) and follow the installation requirements
+
+```python
+from transformers import BertTokenizer, BertForTokenClassification
+import torch
+import torch.nn.functional as F
+from gec.tag import rewrite
+
+
+nopnx_tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/text-editing-qalb14-nopnx')
+nopnx_model = BertForTokenClassification.from_pretrained('CAMeL-Lab/text-editing-qalb14-nopnx')
+
+pnx_tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/text-editing-qalb14-pnx')
+pnx_model = BertForTokenClassification.from_pretrained('CAMeL-Lab/text-editing-qalb14-pnx')
+
+
+def predict(model, tokenizer, text, decode_iter=1):
+    for _ in range(decode_iter):
+        tokenized_text = tokenizer(text, return_tensors="pt", is_split_into_words=True)
+        with torch.no_grad():
+            logits = model(**tokenized_text).logits
+            preds = F.softmax(logits.squeeze(), dim=-1)
+            preds = torch.argmax(preds, dim=-1).cpu().numpy()
+            edits = [model.config.id2label[p] for p in preds[1:-1]]
+            
+            assert len(edits) == len(tokenized_text['input_ids'][0][1:-1])
+        subwords = tokenizer.convert_ids_to_tokens(tokenized_text['input_ids'][0][1:-1])
+        text = rewrite(subwords=[subwords], edits=[edits])[0][0]
+    return text
+
+
+text = 'يجب الإهتمام ب الصحه و لا سيما ف ي الصحه النفسيه ياشباب المستقبل،،'.split()
+
+output_sent = predict(nopnx_model, nopnx_tokenizer, text, decode_iter=2)
+output_sent = predict(pnx_model, pnx_tokenizer, output_sent.split(), decode_iter=1)
+print(output_sent) # يجب الاهتمام بالصحة ولا سيما في الصحة النفسية يا شباب المستقبل .
+
+```
+
 
 
 ## Citation