cfli
/

LLARA-passage

@@ -1,201 +1,94 @@
----
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-tags: []
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+# LLARA-7B-Passage
+This model is fine-tuned from LLaMA-2-7B using LoRA and the embedding size is 4096.
+## Training Data
+The model is fine-tuned on the training split of [MS MARCO Passage Ranking](https://microsoft.github.io/msmarco/Datasets) datasets for 1 epoch. Please check our paper for details.
+## Usage
+Below is an example to encode a query and a passage, and then compute their similarity using their embedding.
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer, LlamaModel
+def get_query_inputs(queries, tokenizer, max_length=512):
+    prefix = '"'
+    suffix = '", predict the following passage within eight words: <s9><s10><s11><s12><s13><s14><s15><s16>'
+    prefix_ids = tokenizer(prefix, return_tensors=None)['input_ids']
+    suffix_ids = tokenizer(suffix, return_tensors=None)['input_ids'][1:]
+    queries_inputs = []
+    for query in queries:
+        inputs = tokenizer(query,
+                           return_tensors=None,
+                           max_length=max_length,
+                           truncation=True,
+                           add_special_tokens=False)
+        inputs['input_ids'] = prefix_ids + inputs['input_ids'] + suffix_ids
+        inputs['attention_mask'] = [1] * len(inputs['input_ids'])
+        queries_inputs.append(inputs)
+    return tokenizer.pad(
+            queries_inputs,
+            padding=True,
+            max_length=max_length,
+            pad_to_multiple_of=8,
+            return_tensors='pt',
+        )
+def get_passage_inputs(passages, tokenizer, max_length=512):
+    prefix = '"'
+    suffix = '", summarize the above passage within eight words: <s1><s2><s3><s4><s5><s6><s7><s8>'
+    prefix_ids = tokenizer(prefix, return_tensors=None)['input_ids']
+    suffix_ids = tokenizer(suffix, return_tensors=None)['input_ids'][1:]
+    passages_inputs = []
+    for passage in passages:
+        inputs = tokenizer(passage,
+                           return_tensors=None,
+                           max_length=max_length,
+                           truncation=True,
+                           add_special_tokens=False)
+        inputs['input_ids'] = prefix_ids + inputs['input_ids'] + suffix_ids
+        inputs['attention_mask'] = [1] * len(inputs['input_ids'])
+        passages_inputs.append(inputs)
+    return tokenizer.pad(
+            passages_inputs,
+            padding=True,
+            max_length=max_length,
+            pad_to_multiple_of=8,
+            return_tensors='pt',
+        )
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('cfli/LLARA-passage')
+model = AutoModel.from_pretrained('cfli/LLARA-passage')
+# Define query and passage inputs
+query = "What is llama?"
+title = "Llama"
+passage = "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era."
+query_input = get_query_inputs([query], tokenizer)
+passage_input = get_passage_inputs([passage], tokenizer)
+with torch.no_grad():
+    # compute query embedding
+    query_outputs = model(**query_input, return_dict=True, output_hidden_states=True)
+    query_embedding = query_outputs.hidden_states[-1][:, -8:, :]
+    query_embedding = torch.mean(query_embedding, dim=1)
+    query_embedding = torch.nn.functional.normalize(query_embedding, dim=-1)
+    # compute passage embedding
+    passage_outputs = model(**passage_input, return_dict=True, output_hidden_states=True)
+    passage_embeddings = passage_outputs.hidden_states[-1][:, -8:, :]
+    passage_embeddings = torch.mean(passage_embeddings, dim=1)
+    passage_embeddings = torch.nn.functional.normalize(passage_embeddings, dim=-1)
+    # compute similarity score
+    score = query_embedding @ passage_embeddings.T
+    print(score)
+```