Update README.md

README.md

@@ -19,18 +19,18 @@ import torch
 def load_finetuned_model():
 
 
-        sentence_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-sentence-pubmed")
-        query_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-query-pubmed")
+        sentence_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-sentence-pubmed", revision="71f4539120e29024adc618173a1ed5fd230ac249")
+        query_encoder = AutoModel.from_pretrained("biu-nlp/abstract-sim-query-pubmed", revision="8d34676d80a39bcbc5a1d2eec13e6f8078496215")
         tokenizer = AutoTokenizer.from_pretrained("biu-nlp/abstract-sim-sentence-pubmed")
-
         return tokenizer, query_encoder, sentence_encoder
 
 
 def encode_batch(model, tokenizer, sentences, device):
-    input_ids = tokenizer(sentences, padding=True, max_length=512, truncation=True, return_tensors="pt",
+    input_ids = tokenizer(sentences, padding=True, max_length=128, truncation=True, return_tensors="pt",
                           add_special_tokens=True).to(device)
     features = model(**input_ids)[0]
     features =  torch.sum(features[:,1:,:] * input_ids["attention_mask"][:,1:].unsqueeze(-1), dim=1) / torch.clamp(torch.sum(input_ids["attention_mask"][:,1:], dim=1, keepdims=True), min=1e-9)
+        
     return features
 
 ```