asahi417 commited on
Commit
838a4fe
1 Parent(s): d3c128c

model update

Browse files
Files changed (1) hide show
  1. README.md +86 -86
README.md CHANGED
@@ -48,252 +48,252 @@ model-index:
48
  value: 0.6499011626820898
49
  - name: QAAlignedF1Score (BERTScore)
50
  type: qa_aligned_f1_score_bertscore
51
- value: 0.9553719665829591
52
  - name: QAAlignedRecall (BERTScore)
53
  type: qa_aligned_recall_bertscore
54
- value: 0.9553719676636558
55
  - name: QAAlignedPrecision (BERTScore)
56
  type: qa_aligned_precision_bertscore
57
- value: 0.9553719676636558
58
  - name: QAAlignedF1Score (MoverScore)
59
  type: qa_aligned_f1_score_moverscore
60
- value: 0.7082452551815105
61
  - name: QAAlignedRecall (MoverScore)
62
  type: qa_aligned_recall_moverscore
63
- value: 0.7082445720362622
64
  - name: QAAlignedPrecision (MoverScore)
65
  type: qa_aligned_precision_moverscore
66
- value: 0.7082445720362622
67
  - task:
68
  name: Text2text Generation
69
  type: text2text-generation
70
  dataset:
71
- name: lmqg/qg_subjqa
72
- type: tripadvisor
73
- args: tripadvisor
74
  metrics:
75
  - name: BLEU4
76
  type: bleu4
77
- value: 8.380171318718442e-07
78
  - name: ROUGE-L
79
  type: rouge-l
80
- value: 0.1402922852924756
81
  - name: METEOR
82
  type: meteor
83
- value: 0.1372146070365174
84
  - name: BERTScore
85
  type: bertscore
86
- value: 0.8891002409937424
87
  - name: MoverScore
88
  type: moverscore
89
- value: 0.5604572211470809
90
  - task:
91
  name: Text2text Generation
92
  type: text2text-generation
93
  dataset:
94
  name: lmqg/qg_squadshifts
95
- type: amazon
96
- args: amazon
97
  metrics:
98
  - name: BLEU4
99
  type: bleu4
100
- value: 0.06530369842068952
101
  - name: ROUGE-L
102
  type: rouge-l
103
- value: 0.25030985091008146
104
  - name: METEOR
105
  type: meteor
106
- value: 0.2229994442645732
107
  - name: BERTScore
108
  type: bertscore
109
- value: 0.9092814804525936
110
  - name: MoverScore
111
  type: moverscore
112
- value: 0.6086538514008419
113
  - task:
114
  name: Text2text Generation
115
  type: text2text-generation
116
  dataset:
117
  name: lmqg/qg_subjqa
118
- type: books
119
- args: books
120
  metrics:
121
  - name: BLEU4
122
  type: bleu4
123
- value: 0.006278914808207679
124
  - name: ROUGE-L
125
  type: rouge-l
126
- value: 0.12368226019088967
127
  - name: METEOR
128
  type: meteor
129
- value: 0.11576293675813865
130
  - name: BERTScore
131
  type: bertscore
132
- value: 0.8807110440044503
133
  - name: MoverScore
134
  type: moverscore
135
- value: 0.5555905941686486
136
  - task:
137
  name: Text2text Generation
138
  type: text2text-generation
139
  dataset:
140
- name: lmqg/qg_subjqa
141
- type: restaurants
142
- args: restaurants
143
  metrics:
144
  - name: BLEU4
145
  type: bleu4
146
- value: 1.1301750984972448e-06
147
  - name: ROUGE-L
148
  type: rouge-l
149
- value: 0.13083168975354642
150
  - name: METEOR
151
  type: meteor
152
- value: 0.12419733006916912
153
  - name: BERTScore
154
  type: bertscore
155
- value: 0.8797711839570719
156
  - name: MoverScore
157
  type: moverscore
158
- value: 0.5542757411268555
159
  - task:
160
  name: Text2text Generation
161
  type: text2text-generation
162
  dataset:
163
  name: lmqg/qg_subjqa
164
- type: movies
165
- args: movies
166
  metrics:
167
  - name: BLEU4
168
  type: bleu4
169
- value: 1.0121579426501661e-06
170
  - name: ROUGE-L
171
  type: rouge-l
172
- value: 0.12508697028506718
173
  - name: METEOR
174
  type: meteor
175
- value: 0.11862284941640638
176
  - name: BERTScore
177
  type: bertscore
178
- value: 0.8748829724726739
179
  - name: MoverScore
180
  type: moverscore
181
- value: 0.5528899173535703
182
  - task:
183
  name: Text2text Generation
184
  type: text2text-generation
185
  dataset:
186
  name: lmqg/qg_subjqa
187
- type: grocery
188
- args: grocery
189
  metrics:
190
  - name: BLEU4
191
  type: bleu4
192
- value: 0.00528043272450429
193
  - name: ROUGE-L
194
  type: rouge-l
195
- value: 0.12343711316491492
196
  - name: METEOR
197
  type: meteor
198
- value: 0.15133496445452477
199
  - name: BERTScore
200
  type: bertscore
201
- value: 0.8778951253890991
202
  - name: MoverScore
203
  type: moverscore
204
- value: 0.5701949938103265
205
  - task:
206
  name: Text2text Generation
207
  type: text2text-generation
208
  dataset:
209
- name: lmqg/qg_squadshifts
210
- type: nyt
211
- args: nyt
212
  metrics:
213
  - name: BLEU4
214
  type: bleu4
215
- value: 0.08117757543966063
216
  - name: ROUGE-L
217
  type: rouge-l
218
- value: 0.25292097720734297
219
  - name: METEOR
220
  type: meteor
221
- value: 0.25254205113198686
222
  - name: BERTScore
223
  type: bertscore
224
- value: 0.9249009759439454
225
  - name: MoverScore
226
  type: moverscore
227
- value: 0.6406329128556304
228
  - task:
229
  name: Text2text Generation
230
  type: text2text-generation
231
  dataset:
232
  name: lmqg/qg_subjqa
233
- type: electronics
234
- args: electronics
235
  metrics:
236
  - name: BLEU4
237
  type: bleu4
238
- value: 0.00866799444965211
239
  - name: ROUGE-L
240
  type: rouge-l
241
- value: 0.1601628874804186
242
  - name: METEOR
243
  type: meteor
244
- value: 0.15348605312210778
245
  - name: BERTScore
246
  type: bertscore
247
- value: 0.8783386920680519
248
  - name: MoverScore
249
  type: moverscore
250
- value: 0.5634845371093992
251
  - task:
252
  name: Text2text Generation
253
  type: text2text-generation
254
  dataset:
255
- name: lmqg/qg_squadshifts
256
- type: new_wiki
257
- args: new_wiki
258
  metrics:
259
  - name: BLEU4
260
  type: bleu4
261
- value: 0.11118273173452982
262
  - name: ROUGE-L
263
  type: rouge-l
264
- value: 0.2967546690273089
265
  - name: METEOR
266
  type: meteor
267
- value: 0.27315087810722966
268
  - name: BERTScore
269
  type: bertscore
270
- value: 0.9322739617807421
271
  - name: MoverScore
272
  type: moverscore
273
- value: 0.6623000084761579
274
  - task:
275
  name: Text2text Generation
276
  type: text2text-generation
277
  dataset:
278
  name: lmqg/qg_squadshifts
279
- type: reddit
280
- args: reddit
281
  metrics:
282
  - name: BLEU4
283
  type: bleu4
284
- value: 0.059525104157825456
285
  - name: ROUGE-L
286
  type: rouge-l
287
- value: 0.22365090580055863
288
  - name: METEOR
289
  type: meteor
290
- value: 0.21499800504546457
291
  - name: BERTScore
292
  type: bertscore
293
- value: 0.9095144685254328
294
  - name: MoverScore
295
  type: moverscore
296
- value: 0.6059332247878408
297
  ---
298
 
299
  # Model Card of `lmqg/bart-large-squad`
@@ -372,16 +372,16 @@ question = pipe('<hl> Beyonce <hl> further expanded her acting career, starring
372
 
373
  | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
374
  |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
 
 
375
  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | tripadvisor | 0.0 | 0.14 | 0.137 | 0.889 | 0.56 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) |
376
- | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | amazon | 0.065 | 0.25 | 0.223 | 0.909 | 0.609 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) |
377
- | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | books | 0.006 | 0.124 | 0.116 | 0.881 | 0.556 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) |
378
  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | restaurants | 0.0 | 0.131 | 0.124 | 0.88 | 0.554 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) |
 
 
379
  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | movies | 0.0 | 0.125 | 0.119 | 0.875 | 0.553 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) |
380
  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | grocery | 0.005 | 0.123 | 0.151 | 0.878 | 0.57 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) |
381
- | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | nyt | 0.081 | 0.253 | 0.253 | 0.925 | 0.641 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) |
382
- | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | electronics | 0.009 | 0.16 | 0.153 | 0.878 | 0.563 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) |
383
- | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | new_wiki | 0.111 | 0.297 | 0.273 | 0.932 | 0.662 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) |
384
- | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | reddit | 0.06 | 0.224 | 0.215 | 0.91 | 0.606 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) |
385
 
386
 
387
  ## Training hyperparameters
 
48
  value: 0.6499011626820898
49
  - name: QAAlignedF1Score (BERTScore)
50
  type: qa_aligned_f1_score_bertscore
51
+ value: 0.9553719667577645
52
  - name: QAAlignedRecall (BERTScore)
53
  type: qa_aligned_recall_bertscore
54
+ value: 0.9548501701962565
55
  - name: QAAlignedPrecision (BERTScore)
56
  type: qa_aligned_precision_bertscore
57
+ value: 0.9559103034487555
58
  - name: QAAlignedF1Score (MoverScore)
59
  type: qa_aligned_f1_score_moverscore
60
+ value: 0.708244616864839
61
  - name: QAAlignedRecall (MoverScore)
62
  type: qa_aligned_recall_moverscore
63
+ value: 0.7053901691540012
64
  - name: QAAlignedPrecision (MoverScore)
65
  type: qa_aligned_precision_moverscore
66
+ value: 0.7112501965782075
67
  - task:
68
  name: Text2text Generation
69
  type: text2text-generation
70
  dataset:
71
+ name: lmqg/qg_squadshifts
72
+ type: reddit
73
+ args: reddit
74
  metrics:
75
  - name: BLEU4
76
  type: bleu4
77
+ value: 0.059525104157825456
78
  - name: ROUGE-L
79
  type: rouge-l
80
+ value: 0.22365090580055863
81
  - name: METEOR
82
  type: meteor
83
+ value: 0.21499800504546457
84
  - name: BERTScore
85
  type: bertscore
86
+ value: 0.9095144685254328
87
  - name: MoverScore
88
  type: moverscore
89
+ value: 0.6059332247878408
90
  - task:
91
  name: Text2text Generation
92
  type: text2text-generation
93
  dataset:
94
  name: lmqg/qg_squadshifts
95
+ type: new_wiki
96
+ args: new_wiki
97
  metrics:
98
  - name: BLEU4
99
  type: bleu4
100
+ value: 0.11118273173452982
101
  - name: ROUGE-L
102
  type: rouge-l
103
+ value: 0.2967546690273089
104
  - name: METEOR
105
  type: meteor
106
+ value: 0.27315087810722966
107
  - name: BERTScore
108
  type: bertscore
109
+ value: 0.9322739617807421
110
  - name: MoverScore
111
  type: moverscore
112
+ value: 0.6623000084761579
113
  - task:
114
  name: Text2text Generation
115
  type: text2text-generation
116
  dataset:
117
  name: lmqg/qg_subjqa
118
+ type: tripadvisor
119
+ args: tripadvisor
120
  metrics:
121
  - name: BLEU4
122
  type: bleu4
123
+ value: 8.380171318718442e-07
124
  - name: ROUGE-L
125
  type: rouge-l
126
+ value: 0.1402922852924756
127
  - name: METEOR
128
  type: meteor
129
+ value: 0.1372146070365174
130
  - name: BERTScore
131
  type: bertscore
132
+ value: 0.8891002409937424
133
  - name: MoverScore
134
  type: moverscore
135
+ value: 0.5604572211470809
136
  - task:
137
  name: Text2text Generation
138
  type: text2text-generation
139
  dataset:
140
+ name: lmqg/qg_squadshifts
141
+ type: nyt
142
+ args: nyt
143
  metrics:
144
  - name: BLEU4
145
  type: bleu4
146
+ value: 0.08117757543966063
147
  - name: ROUGE-L
148
  type: rouge-l
149
+ value: 0.25292097720734297
150
  - name: METEOR
151
  type: meteor
152
+ value: 0.25254205113198686
153
  - name: BERTScore
154
  type: bertscore
155
+ value: 0.9249009759439454
156
  - name: MoverScore
157
  type: moverscore
158
+ value: 0.6406329128556304
159
  - task:
160
  name: Text2text Generation
161
  type: text2text-generation
162
  dataset:
163
  name: lmqg/qg_subjqa
164
+ type: restaurants
165
+ args: restaurants
166
  metrics:
167
  - name: BLEU4
168
  type: bleu4
169
+ value: 1.1301750984972448e-06
170
  - name: ROUGE-L
171
  type: rouge-l
172
+ value: 0.13083168975354642
173
  - name: METEOR
174
  type: meteor
175
+ value: 0.12419733006916912
176
  - name: BERTScore
177
  type: bertscore
178
+ value: 0.8797711839570719
179
  - name: MoverScore
180
  type: moverscore
181
+ value: 0.5542757411268555
182
  - task:
183
  name: Text2text Generation
184
  type: text2text-generation
185
  dataset:
186
  name: lmqg/qg_subjqa
187
+ type: electronics
188
+ args: electronics
189
  metrics:
190
  - name: BLEU4
191
  type: bleu4
192
+ value: 0.00866799444965211
193
  - name: ROUGE-L
194
  type: rouge-l
195
+ value: 0.1601628874804186
196
  - name: METEOR
197
  type: meteor
198
+ value: 0.15348605312210778
199
  - name: BERTScore
200
  type: bertscore
201
+ value: 0.8783386920680519
202
  - name: MoverScore
203
  type: moverscore
204
+ value: 0.5634845371093992
205
  - task:
206
  name: Text2text Generation
207
  type: text2text-generation
208
  dataset:
209
+ name: lmqg/qg_subjqa
210
+ type: books
211
+ args: books
212
  metrics:
213
  - name: BLEU4
214
  type: bleu4
215
+ value: 0.006278914808207679
216
  - name: ROUGE-L
217
  type: rouge-l
218
+ value: 0.12368226019088967
219
  - name: METEOR
220
  type: meteor
221
+ value: 0.11576293675813865
222
  - name: BERTScore
223
  type: bertscore
224
+ value: 0.8807110440044503
225
  - name: MoverScore
226
  type: moverscore
227
+ value: 0.5555905941686486
228
  - task:
229
  name: Text2text Generation
230
  type: text2text-generation
231
  dataset:
232
  name: lmqg/qg_subjqa
233
+ type: movies
234
+ args: movies
235
  metrics:
236
  - name: BLEU4
237
  type: bleu4
238
+ value: 1.0121579426501661e-06
239
  - name: ROUGE-L
240
  type: rouge-l
241
+ value: 0.12508697028506718
242
  - name: METEOR
243
  type: meteor
244
+ value: 0.11862284941640638
245
  - name: BERTScore
246
  type: bertscore
247
+ value: 0.8748829724726739
248
  - name: MoverScore
249
  type: moverscore
250
+ value: 0.5528899173535703
251
  - task:
252
  name: Text2text Generation
253
  type: text2text-generation
254
  dataset:
255
+ name: lmqg/qg_subjqa
256
+ type: grocery
257
+ args: grocery
258
  metrics:
259
  - name: BLEU4
260
  type: bleu4
261
+ value: 0.00528043272450429
262
  - name: ROUGE-L
263
  type: rouge-l
264
+ value: 0.12343711316491492
265
  - name: METEOR
266
  type: meteor
267
+ value: 0.15133496445452477
268
  - name: BERTScore
269
  type: bertscore
270
+ value: 0.8778951253890991
271
  - name: MoverScore
272
  type: moverscore
273
+ value: 0.5701949938103265
274
  - task:
275
  name: Text2text Generation
276
  type: text2text-generation
277
  dataset:
278
  name: lmqg/qg_squadshifts
279
+ type: amazon
280
+ args: amazon
281
  metrics:
282
  - name: BLEU4
283
  type: bleu4
284
+ value: 0.06530369842068952
285
  - name: ROUGE-L
286
  type: rouge-l
287
+ value: 0.25030985091008146
288
  - name: METEOR
289
  type: meteor
290
+ value: 0.2229994442645732
291
  - name: BERTScore
292
  type: bertscore
293
+ value: 0.9092814804525936
294
  - name: MoverScore
295
  type: moverscore
296
+ value: 0.6086538514008419
297
  ---
298
 
299
  # Model Card of `lmqg/bart-large-squad`
 
372
 
373
  | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
374
  |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
375
+ | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | reddit | 0.06 | 0.224 | 0.215 | 0.91 | 0.606 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) |
376
+ | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | new_wiki | 0.111 | 0.297 | 0.273 | 0.932 | 0.662 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) |
377
  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | tripadvisor | 0.0 | 0.14 | 0.137 | 0.889 | 0.56 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) |
378
+ | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | nyt | 0.081 | 0.253 | 0.253 | 0.925 | 0.641 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) |
 
379
  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | restaurants | 0.0 | 0.131 | 0.124 | 0.88 | 0.554 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) |
380
+ | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | electronics | 0.009 | 0.16 | 0.153 | 0.878 | 0.563 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) |
381
+ | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | books | 0.006 | 0.124 | 0.116 | 0.881 | 0.556 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) |
382
  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | movies | 0.0 | 0.125 | 0.119 | 0.875 | 0.553 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) |
383
  | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | grocery | 0.005 | 0.123 | 0.151 | 0.878 | 0.57 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) |
384
+ | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | amazon | 0.065 | 0.25 | 0.223 | 0.909 | 0.609 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) |
 
 
 
385
 
386
 
387
  ## Training hyperparameters