Update README.md
Browse files
README.md
CHANGED
@@ -14,40 +14,46 @@ Below is an example to encode a query and a passage, and then compute their simi
|
|
14 |
import torch
|
15 |
from transformers import AutoModel, AutoTokenizer, LlamaModel
|
16 |
|
17 |
-
def get_query_inputs(
|
18 |
prefix = '"'
|
19 |
suffix = '", predict the following passage within eight words: <s9><s10><s11><s12><s13><s14><s15><s16>'
|
20 |
prefix_ids = tokenizer(prefix, return_tensors=None)['input_ids']
|
21 |
suffix_ids = tokenizer(suffix, return_tensors=None)['input_ids'][1:]
|
22 |
-
|
23 |
-
|
24 |
-
|
25 |
-
|
26 |
-
|
27 |
-
|
28 |
-
|
|
|
|
|
|
|
29 |
return tokenizer.pad(
|
30 |
-
|
31 |
padding=True,
|
32 |
max_length=max_length,
|
33 |
pad_to_multiple_of=8,
|
34 |
return_tensors='pt',
|
35 |
)
|
36 |
|
37 |
-
def get_passage_inputs(
|
38 |
prefix = '"'
|
39 |
suffix = '", summarize the above passage within eight words: <s1><s2><s3><s4><s5><s6><s7><s8>'
|
40 |
prefix_ids = tokenizer(prefix, return_tensors=None)['input_ids']
|
41 |
suffix_ids = tokenizer(suffix, return_tensors=None)['input_ids'][1:]
|
42 |
-
|
43 |
-
|
44 |
-
|
45 |
-
|
46 |
-
|
47 |
-
|
48 |
-
|
|
|
|
|
|
|
49 |
return tokenizer.pad(
|
50 |
-
|
51 |
padding=True,
|
52 |
max_length=max_length,
|
53 |
pad_to_multiple_of=8,
|
@@ -62,8 +68,8 @@ model = AutoModel.from_pretrained('cfli/LLARA-beir')
|
|
62 |
query = "What is llama?"
|
63 |
title = "Llama"
|
64 |
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."
|
65 |
-
query_input = get_query_inputs(query, tokenizer)
|
66 |
-
passage_input = get_passage_inputs(passage, tokenizer)
|
67 |
|
68 |
|
69 |
with torch.no_grad():
|
@@ -84,4 +90,5 @@ with torch.no_grad():
|
|
84 |
print(score)
|
85 |
|
86 |
|
|
|
87 |
```
|
|
|
14 |
import torch
|
15 |
from transformers import AutoModel, AutoTokenizer, LlamaModel
|
16 |
|
17 |
+
def get_query_inputs(queries, tokenizer, max_length=512):
|
18 |
prefix = '"'
|
19 |
suffix = '", predict the following passage within eight words: <s9><s10><s11><s12><s13><s14><s15><s16>'
|
20 |
prefix_ids = tokenizer(prefix, return_tensors=None)['input_ids']
|
21 |
suffix_ids = tokenizer(suffix, return_tensors=None)['input_ids'][1:]
|
22 |
+
queries_inputs = []
|
23 |
+
for query in queries:
|
24 |
+
inputs = tokenizer(query,
|
25 |
+
return_tensors=None,
|
26 |
+
max_length=max_length,
|
27 |
+
truncation=True,
|
28 |
+
add_special_tokens=False)
|
29 |
+
inputs['input_ids'] = prefix_ids + inputs['input_ids'] + suffix_ids
|
30 |
+
inputs['attention_mask'] = [1] * len(inputs['input_ids'])
|
31 |
+
queries_inputs.append(inputs)
|
32 |
return tokenizer.pad(
|
33 |
+
queries_inputs,
|
34 |
padding=True,
|
35 |
max_length=max_length,
|
36 |
pad_to_multiple_of=8,
|
37 |
return_tensors='pt',
|
38 |
)
|
39 |
|
40 |
+
def get_passage_inputs(passages, tokenizer, max_length=512):
|
41 |
prefix = '"'
|
42 |
suffix = '", summarize the above passage within eight words: <s1><s2><s3><s4><s5><s6><s7><s8>'
|
43 |
prefix_ids = tokenizer(prefix, return_tensors=None)['input_ids']
|
44 |
suffix_ids = tokenizer(suffix, return_tensors=None)['input_ids'][1:]
|
45 |
+
passages_inputs = []
|
46 |
+
for passage in passages:
|
47 |
+
inputs = tokenizer(passage,
|
48 |
+
return_tensors=None,
|
49 |
+
max_length=max_length,
|
50 |
+
truncation=True,
|
51 |
+
add_special_tokens=False)
|
52 |
+
inputs['input_ids'] = prefix_ids + inputs['input_ids'] + suffix_ids
|
53 |
+
inputs['attention_mask'] = [1] * len(inputs['input_ids'])
|
54 |
+
passages_inputs.append(inputs)
|
55 |
return tokenizer.pad(
|
56 |
+
passages_inputs,
|
57 |
padding=True,
|
58 |
max_length=max_length,
|
59 |
pad_to_multiple_of=8,
|
|
|
68 |
query = "What is llama?"
|
69 |
title = "Llama"
|
70 |
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."
|
71 |
+
query_input = get_query_inputs([query], tokenizer)
|
72 |
+
passage_input = get_passage_inputs([passage], tokenizer)
|
73 |
|
74 |
|
75 |
with torch.no_grad():
|
|
|
90 |
print(score)
|
91 |
|
92 |
|
93 |
+
|
94 |
```
|