Update QAPipeline.py
Browse files- QAPipeline.py +20 -13
QAPipeline.py
CHANGED
@@ -1,9 +1,4 @@
|
|
1 |
-
# qapipeline.py
|
2 |
-
|
3 |
-
from transformers import PreTrainedModel, Pipeline
|
4 |
-
from typing import Any, Dict
|
5 |
from transformers import Pipeline
|
6 |
-
from transformers import PreTrainedTokenizer
|
7 |
from transformers.utils import ModelOutput
|
8 |
|
9 |
from transformers import PreTrainedModel, Pipeline
|
@@ -13,7 +8,6 @@ class QApipeline(Pipeline):
|
|
13 |
def __init__(
|
14 |
self,
|
15 |
model: PreTrainedModel,
|
16 |
-
tokenizer: PreTrainedTokenizer,
|
17 |
**kwargs
|
18 |
):
|
19 |
super().__init__(
|
@@ -23,30 +17,43 @@ class QApipeline(Pipeline):
|
|
23 |
|
24 |
print("in __init__")
|
25 |
|
26 |
-
def __call__( self,
|
27 |
-
|
|
|
|
|
|
|
|
|
|
|
28 |
answer = self._process_output(outputs)
|
|
|
29 |
print("in __call___")
|
30 |
-
|
|
|
31 |
|
32 |
def _process_output(self, outputs: Any) -> str:
|
|
|
33 |
print("in process outputs")
|
|
|
|
|
34 |
format = {'guess': outputs[1], 'confidence': outputs[0]}
|
35 |
return format
|
36 |
-
|
|
|
37 |
def _sanitize_parameters(self, **kwargs):
|
38 |
-
print("in
|
|
|
39 |
return {}, {}, {}
|
40 |
|
41 |
def preprocess(self, inputs):
|
42 |
print("in preprocess")
|
|
|
43 |
return inputs
|
44 |
|
45 |
def postprocess(self, outputs):
|
46 |
print("in postprocess")
|
47 |
format = {'guess': outputs[1], 'confidence': float(outputs[0])}
|
48 |
return format
|
49 |
-
|
50 |
def _forward(self, input_tensors, **forward_parameters: Dict) -> ModelOutput:
|
51 |
print("in _forward")
|
52 |
-
return super()._forward(input_tensors, **forward_parameters)
|
|
|
|
|
|
|
|
|
|
|
1 |
from transformers import Pipeline
|
|
|
2 |
from transformers.utils import ModelOutput
|
3 |
|
4 |
from transformers import PreTrainedModel, Pipeline
|
|
|
8 |
def __init__(
|
9 |
self,
|
10 |
model: PreTrainedModel,
|
|
|
11 |
**kwargs
|
12 |
):
|
13 |
super().__init__(
|
|
|
17 |
|
18 |
print("in __init__")
|
19 |
|
20 |
+
def __call__( self, question: str, **kwargs) -> Dict[str, Any]:
|
21 |
+
inputs = {
|
22 |
+
"question": question,
|
23 |
+
}
|
24 |
+
|
25 |
+
outputs = self.model.predict(question)
|
26 |
+
|
27 |
answer = self._process_output(outputs)
|
28 |
+
|
29 |
print("in __call___")
|
30 |
+
|
31 |
+
return answer
|
32 |
|
33 |
def _process_output(self, outputs: Any) -> str:
|
34 |
+
|
35 |
print("in process outputs")
|
36 |
+
print(outputs)
|
37 |
+
|
38 |
format = {'guess': outputs[1], 'confidence': outputs[0]}
|
39 |
return format
|
40 |
+
|
41 |
+
|
42 |
def _sanitize_parameters(self, **kwargs):
|
43 |
+
print("in sanatize params")
|
44 |
+
|
45 |
return {}, {}, {}
|
46 |
|
47 |
def preprocess(self, inputs):
|
48 |
print("in preprocess")
|
49 |
+
|
50 |
return inputs
|
51 |
|
52 |
def postprocess(self, outputs):
|
53 |
print("in postprocess")
|
54 |
format = {'guess': outputs[1], 'confidence': float(outputs[0])}
|
55 |
return format
|
56 |
+
|
57 |
def _forward(self, input_tensors, **forward_parameters: Dict) -> ModelOutput:
|
58 |
print("in _forward")
|
59 |
+
return super()._forward(input_tensors, **forward_parameters)
|