Create README.md

README.md

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+---
+license: cc-by-3.0
+language:
+- en
+---
+
+A model for mapping abstract sentence descriptions to sentences that fit the descriptions. Trained on Pubmed sentences. Use ```load_finetuned_model``` to load the query and sentence encoder, and ```encode_batch()``` to encode a sentence with the model.
+
+```python
+
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+def load_finetuned_model():
+
+
+        sentence_encoder = AutoModel.from_pretrained("ravfogs/abstract-sim-sentence-pubmed")
+        query_encoder = AutoModel.from_pretrained("ravfogs/abstract-sim-query-pubmed")
+        tokenizer = AutoTokenizer.from_pretrained("ravfogs/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",
+                          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
+
+```