Update README.md
Browse files
README.md
CHANGED
@@ -18,10 +18,77 @@ Use https://github.com/nlmatics/nlm-utils
|
|
18 |
|
19 |
Click on each model to see details:
|
20 |
|
21 |
-
|
22 |
-
|
23 |
-
|
24 |
-
|
25 |
-
|
26 |
-
|
27 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
18 |
|
19 |
Click on each model to see details:
|
20 |
|
21 |
+
### roberta.large.boolq
|
22 |
+
|
23 |
+
Location: [roberta.large.boolq](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.boolq)
|
24 |
+
|
25 |
+
Trained with MNLI + Boolq
|
26 |
+
|
27 |
+
Trained by: Evan Li
|
28 |
+
|
29 |
+
Application: Given a passage and a question, answer the question with yes, no or unsure.
|
30 |
+
|
31 |
+
Training Process: https://blogs.nlmatics.com/2020/03/12/Boolean-Question-Answering-with-Neutral-Labels.html
|
32 |
+
|
33 |
+
### roberta.large.qa
|
34 |
+
See folder: [roberta.large.qa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qa)
|
35 |
+
|
36 |
+
Trained with SQuAD 2.0 + Custom Dataset preferring shorter spans better suited for data extraction
|
37 |
+
|
38 |
+
Trained by: Ambika Sukla
|
39 |
+
|
40 |
+
Application: Given a passage and a question, pick the shortest span from the passage that answers the question
|
41 |
+
|
42 |
+
Training Process: start, end location head on the top of Roberta Base
|
43 |
+
|
44 |
+
### roberta.large.stsb
|
45 |
+
See folder: [roberta.large.stsb](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.stsb)
|
46 |
+
|
47 |
+
Trained with STSB dataset
|
48 |
+
|
49 |
+
Trained by: Meta/Fairseq
|
50 |
+
|
51 |
+
Application: Given two passages, return a score beteen 0 and 1 to evaluate their similarity
|
52 |
+
|
53 |
+
Training Process: regression head on top of Roberta Base
|
54 |
+
|
55 |
+
### roberta.large.phraseqa
|
56 |
+
See folder: [roberta.large.phraseqa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.phraseqa)
|
57 |
+
|
58 |
+
Trained with Roberta 2.0 with the question words removed from the question
|
59 |
+
|
60 |
+
Trained By: Batya Stein
|
61 |
+
|
62 |
+
Application: Given a passage and phrase (key), extract a value from the passage
|
63 |
+
|
64 |
+
Training Process: https://blogs.nlmatics.com/2020/08/25/Optimizing-Transformer-Q&A-Models-for-Naturalistic-Search.html
|
65 |
+
|
66 |
+
### roberta.large.qasrl
|
67 |
+
|
68 |
+
See folder: [roberta.large.qasrl](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qasrl)
|
69 |
+
|
70 |
+
Trained with QASRL dataset
|
71 |
+
|
72 |
+
Application: Given a passage, get back values for who, what, when, where etc.
|
73 |
+
|
74 |
+
Trained By: Nima Sheikholeslami
|
75 |
+
|
76 |
+
### roberta.large.qatype.lower.RothWithQ
|
77 |
+
|
78 |
+
See folder: [roberta.large.qatype.lower.RothWithQ](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.qatype.lower.RothWithQ)
|
79 |
+
|
80 |
+
Trained with the Roth Question Type dataset.
|
81 |
+
|
82 |
+
Application: Given a question, return one of the answer types e.g. number, location. See the Roth dataset for full list.
|
83 |
+
|
84 |
+
Trained By: Evan Li
|
85 |
+
|
86 |
+
### roberta.large.io_qa
|
87 |
+
|
88 |
+
See folder: [roberta.large.io_qa](https://huggingface.co/ansukla/roberta/tree/main/roberta.large.io_qa)
|
89 |
+
Trained with SQuAD 2.0 dataset
|
90 |
+
|
91 |
+
Trained By: Nima Sheikholeslami
|
92 |
+
|
93 |
+
Training Process: Use io head to support multiple spans.
|
94 |
+
|