Hugging Face's logo

philipphager
/
baidu-ultr_uva-bert_naive-listwise

Transformers
Safetensors
bert
Inference Endpoints
Model card Files Community
philipphager commited on
Commit
59d07ef
•
1 Parent(s): 797f9d4

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +14 -14
README.md CHANGED
@@ -25,32 +25,32 @@ model = ListwiseCrossEncoder.from_pretrained(
25
  "philipphager/baidu-ultr_uva-bert_naive-listwise",
26
  )
27
 
28
- # Mock batch from Baidu-ULTR with 4 documents, each with 32 tokens
29
  batch = {
30
  # Query_id for each document
31
  "query_id": jnp.array([1, 1, 1, 1]),
32
  # Document position in SERP
33
  "positions": jnp.array([1, 2, 3, 4]),
34
- # Token ids for each query/document combination
35
  "tokens": jnp.array([
36
- [2, 21448, 21874, 21436, 2860, 5996, 9526, 15035, 2677, 21446, 21401, 21401, 1, 20206, 4012, 2860, 5996, 9526, 10966, 11858, 15035, 2677, 21446, 21401, 21401, 10092, 250, 8547, 7936, 2677, 1, 21874],
37
- [2, 21448, 21874, 21436, 2860, 5996, 9526, 15035, 2677, 21446, 21401, 21401, 1, 16794, 4522, 2082, 2860, 16923, 3186, 15035, 2677, 21446, 21401, 21401, 10092, 21448, 19087, 480, 21449, 21401, 8747, 21436],
38
- [2, 21448, 21874, 21436, 2860, 5996, 9526, 15035, 2677, 21446, 21401, 21401, 1, 20206, 10082, 9773, 6164, 8825, 2860, 5996, 9526, 15035, 2677, 21446, 21401, 21401, 10092, 21455, 4516, 2049, 20167, 15035],
39
- [2, 21448, 21874, 21436, 2860, 5996, 9526, 15035, 2677, 21446, 21401, 21401, 1, 2618, 8520, 2860, 5996, 9526, 15035, 2677, 21446, 21401, 21401, 10092, 21455, 2618, 8520, 2860, 5996, 9526, 21446, 21401],
40
  ]),
41
  # Specify if a token id belongs to the query (0) or document (1)
42
  "token_types": jnp.array([
43
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
44
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
45
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
46
- [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
47
  ]),
48
  # Marks if a token should be attended to (True) or ignored, e.g., padding tokens (False):
49
  "attention_mask": jnp.array([
50
- [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True],
51
- [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True],
52
- [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True],
53
- [True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True],
54
  ]),
55
  }
56
 
 
25
  "philipphager/baidu-ultr_uva-bert_naive-listwise",
26
  )
27
 
28
+ # Mock batch following Baidu-ULTR with 4 documents, each with 8 tokens
29
  batch = {
30
  # Query_id for each document
31
  "query_id": jnp.array([1, 1, 1, 1]),
32
  # Document position in SERP
33
  "positions": jnp.array([1, 2, 3, 4]),
34
+ # Token ids for: [CLS] Query [SEP] Document
35
  "tokens": jnp.array([
36
+ [2, 21448, 21874, 21436, 1, 20206, 4012, 2860],
37
+ [2, 21448, 21874, 21436, 1, 16794, 4522, 2082],
38
+ [2, 21448, 21874, 21436, 1, 20206, 10082, 9773],
39
+ [2, 21448, 21874, 21436, 1, 2618, 8520, 2860],
40
  ]),
41
  # Specify if a token id belongs to the query (0) or document (1)
42
  "token_types": jnp.array([
43
+ [0, 0, 0, 0, 1, 1, 1, 1],
44
+ [0, 0, 0, 0, 1, 1, 1, 1],
45
+ [0, 0, 0, 0, 1, 1, 1, 1],
46
+ [0, 0, 0, 0, 1, 1, 1, 1],
47
  ]),
48
  # Marks if a token should be attended to (True) or ignored, e.g., padding tokens (False):
49
  "attention_mask": jnp.array([
50
+ [True, True, True, True, True, True, True, True],
51
+ [True, True, True, True, True, True, True, True],
52
+ [True, True, True, True, True, True, True, True],
53
+ [True, True, True, True, True, True, True, True],
54
  ]),
55
  }
56