Question Answering
Transformers
PyTorch
English
bert
Inference Endpoints
File size: 1,611 Bytes
0297073
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
---
language: 
  - en
tags:
- question-answering
license: "apache-2.0"
datasets:
- squad
- newsqa
- hotpotqa
- searchqa
- triviaqa-web
- naturalquestions
- qamr
- duorc
- boolq
- commonsense_qa
- hellaswag
- race
- social_i_qa
- drop
- narrativeqa
- hybrid_qa
metrics:
- squad
- accuracy
---

# Description
Checkpoint of MetaQA from MetaQA: Combining Expert Agents for Multi-Skill Question Answering (https://arxiv.org/abs/2112.01922)

# How to Use

```
from inference import MetaQA, PredictionRequest
metaqa = MetaQA("haritzpuerto/MetaQA")
# run the QA Agents with the input question and context. For this example, I will show mockup outputs from extractive QA agents.
list_preds = [('Utah', 0.1442876160144806),
                ('DOC] [TLE] 1886', 0.10822545737028122),
                ('Utah Territory', 0.6455602645874023),
                ('Eli Murray opposed the', 0.352359801530838),
                ('Utah', 0.48052430152893066),
                ('Utah Territory', 0.35186105966567993),
                ('Utah', 0.8328599333763123),
                ('Utah', 0.3405868709087372),
            ]
# add ("", 0.0) to the list of predictions until the size is 16 (because MetaQA was trained on 16 datasets/agents including other formats, not only extractive)
for i in range(16-len(list_preds)):
    list_preds.append(("", 0.0))

request = PredictionRequest()
request.input_question = "While serving as Governor of this territory, 1880-1886, Eli Murray opposed the advancement of polygamy?"
request.input_predictions = list_preds

(pred, agent_name, metaqa_score, agent_score) = metaqa.run_metaqa(request)
```