G2PTL Update

README.md

@@ -4,11 +4,11 @@ license: apache-2.0
 ---
 
 
-# G2PTL
+# G2PTL-1
 
 ## Introduction
 
-G2PTL: A Geography-Graph Pre-trained model for address.
+G2PTL-1: A Geography-Graph Pre-trained model for address. This work is the first version of G2PTL (v1.0)
 
 
 ## Model description
@@ -47,8 +47,8 @@ You can use this model directly with a pipeline for masked language modeling:
 
 ```Python
 >>> from transformers import pipeline, AutoModel, AutoTokenizer
->>> model = AutoModel.from_pretrained('JunhongLou/G2PTL', trust_remote_code=True)
->>> tokenizer = AutoTokenizer.from_pretrained('JunhongLou/G2PTL', trust_remote_code=True)
+>>> model = AutoModel.from_pretrained('Cainiao-AI/G2PTL', trust_remote_code=True)
+>>> tokenizer = AutoTokenizer.from_pretrained('Cainiao-AI/G2PTL', trust_remote_code=True)
 
 >>> mask_filler = pipeline(task= 'fill-mask', model= model,tokenizer = tokenizer)
 >>> mask_filler("浙江省杭州市[MASK]杭区五常街道阿里巴巴西溪园区")
@@ -80,8 +80,8 @@ You can also use this model for multiple [MASK] filling in PyTorch:
 ```python
 from transformers import pipeline, AutoModel, AutoTokenizer
 import torch
-model = AutoModel.from_pretrained('JunhongLou/G2PTL', trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained('JunhongLou/G2PTL', trust_remote_code=True)
+model = AutoModel.from_pretrained('Cainiao-AI/G2PTL', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('Cainiao-AI/G2PTL', trust_remote_code=True)
 model.eval()
 text = ['浙江省杭州市[MASK][MASK][MASK]五常街道阿里巴巴西溪园区']
 encoded_input = tokenizer(text, return_tensors='pt')
@@ -101,8 +101,8 @@ Here is how to use this model to get the HTC output of a given text in PyTorch:
 
 ```python
 from transformers import pipeline, AutoModel, AutoTokenizer
-model = AutoModel.from_pretrained('JunhongLou/G2PTL', trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained('JunhongLou/G2PTL', trust_remote_code=True)
+model = AutoModel.from_pretrained('Cainiao-AI/G2PTL', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('Cainiao-AI/G2PTL', trust_remote_code=True)
 model.eval()
 text = "浙江省杭州市五常街道阿里巴巴西溪园区"
 encoded_input = tokenizer(text, return_tensors='pt')
@@ -119,8 +119,8 @@ Here is how to use this model to get the features/embeddings of a given text in
 
 ```python
 from transformers import pipeline, AutoModel, AutoTokenizer
-model = AutoModel.from_pretrained('JunhongLou/G2PTL', trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained('JunhongLou/G2PTL', trust_remote_code=True)
+model = AutoModel.from_pretrained('Cainiao-AI/G2PTL', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('Cainiao-AI/G2PTL', trust_remote_code=True)
 model.eval()
 text = "浙江省杭州市余杭区五常街道阿里巴巴西溪园区"
 encoded_input = tokenizer(text, return_tensors='pt')
@@ -133,8 +133,8 @@ Here is how to use this model to get cosine similarity between two address texts
 ```python
 from transformers import pipeline, AutoModel, AutoTokenizer
 import torch
-model = AutoModel.from_pretrained('JunhongLou/G2PTL', trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained('JunhongLou/G2PTL', trust_remote_code=True)
+model = AutoModel.from_pretrained('Cainiao-AI/G2PTL', trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained('Cainiao-AI/G2PTL', trust_remote_code=True)
 model.eval()
 text = ["浙江省杭州市余杭区五常街道阿里巴巴西溪园区", "浙江省杭州市阿里巴巴西溪园区"]
 encoded_input = tokenizer(text, return_tensors='pt', padding=True)