first commit
Browse files- 1_Pooling/config.json +9 -0
- README.md +1085 -1
- added_tokens.json +7 -0
- config.json +31 -0
- config_sentence_transformers.json +7 -0
- modules.json +20 -0
- pytorch_model.bin +3 -0
- sentence_bert_config.json +4 -0
- special_tokens_map.json +14 -0
- tokenizer.json +0 -0
- tokenizer_config.json +71 -0
- vocab.txt +0 -0
1_Pooling/config.json
ADDED
@@ -0,0 +1,9 @@
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{
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"word_embedding_dimension": 1024,
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"pooling_mode_cls_token": true,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": false
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}
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README.md
CHANGED
@@ -1,3 +1,1087 @@
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---
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3 |
---
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1 |
---
|
2 |
+
tags:
|
3 |
+
- mteb
|
4 |
+
model-index:
|
5 |
+
- name: xiaobu-embedding
|
6 |
+
results:
|
7 |
+
- task:
|
8 |
+
type: STS
|
9 |
+
dataset:
|
10 |
+
type: C-MTEB/AFQMC
|
11 |
+
name: MTEB AFQMC
|
12 |
+
config: default
|
13 |
+
split: validation
|
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+
revision: None
|
15 |
+
metrics:
|
16 |
+
- type: cos_sim_pearson
|
17 |
+
value: 49.37874132528482
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+
- type: cos_sim_spearman
|
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+
value: 54.84722470052176
|
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+
- type: euclidean_pearson
|
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+
value: 53.0495882931575
|
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+
- type: euclidean_spearman
|
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+
value: 54.847727301700665
|
24 |
+
- type: manhattan_pearson
|
25 |
+
value: 53.0632140838278
|
26 |
+
- type: manhattan_spearman
|
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+
value: 54.8744258024692
|
28 |
+
- task:
|
29 |
+
type: STS
|
30 |
+
dataset:
|
31 |
+
type: C-MTEB/ATEC
|
32 |
+
name: MTEB ATEC
|
33 |
+
config: default
|
34 |
+
split: test
|
35 |
+
revision: None
|
36 |
+
metrics:
|
37 |
+
- type: cos_sim_pearson
|
38 |
+
value: 48.15992903013723
|
39 |
+
- type: cos_sim_spearman
|
40 |
+
value: 55.13198035464577
|
41 |
+
- type: euclidean_pearson
|
42 |
+
value: 55.435876753245715
|
43 |
+
- type: euclidean_spearman
|
44 |
+
value: 55.13215936702871
|
45 |
+
- type: manhattan_pearson
|
46 |
+
value: 55.41429518223402
|
47 |
+
- type: manhattan_spearman
|
48 |
+
value: 55.13363087679285
|
49 |
+
- task:
|
50 |
+
type: Classification
|
51 |
+
dataset:
|
52 |
+
type: mteb/amazon_reviews_multi
|
53 |
+
name: MTEB AmazonReviewsClassification (zh)
|
54 |
+
config: zh
|
55 |
+
split: test
|
56 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
57 |
+
metrics:
|
58 |
+
- type: accuracy
|
59 |
+
value: 46.722
|
60 |
+
- type: f1
|
61 |
+
value: 45.039340641893205
|
62 |
+
- task:
|
63 |
+
type: STS
|
64 |
+
dataset:
|
65 |
+
type: C-MTEB/BQ
|
66 |
+
name: MTEB BQ
|
67 |
+
config: default
|
68 |
+
split: test
|
69 |
+
revision: None
|
70 |
+
metrics:
|
71 |
+
- type: cos_sim_pearson
|
72 |
+
value: 63.517830355554224
|
73 |
+
- type: cos_sim_spearman
|
74 |
+
value: 65.57007801018649
|
75 |
+
- type: euclidean_pearson
|
76 |
+
value: 64.05153340906585
|
77 |
+
- type: euclidean_spearman
|
78 |
+
value: 65.5696865661119
|
79 |
+
- type: manhattan_pearson
|
80 |
+
value: 63.95710619755406
|
81 |
+
- type: manhattan_spearman
|
82 |
+
value: 65.48565785379489
|
83 |
+
- task:
|
84 |
+
type: Clustering
|
85 |
+
dataset:
|
86 |
+
type: C-MTEB/CLSClusteringP2P
|
87 |
+
name: MTEB CLSClusteringP2P
|
88 |
+
config: default
|
89 |
+
split: test
|
90 |
+
revision: None
|
91 |
+
metrics:
|
92 |
+
- type: v_measure
|
93 |
+
value: 43.24046498507819
|
94 |
+
- task:
|
95 |
+
type: Clustering
|
96 |
+
dataset:
|
97 |
+
type: C-MTEB/CLSClusteringS2S
|
98 |
+
name: MTEB CLSClusteringS2S
|
99 |
+
config: default
|
100 |
+
split: test
|
101 |
+
revision: None
|
102 |
+
metrics:
|
103 |
+
- type: v_measure
|
104 |
+
value: 41.22618199372116
|
105 |
+
- task:
|
106 |
+
type: Reranking
|
107 |
+
dataset:
|
108 |
+
type: C-MTEB/CMedQAv1-reranking
|
109 |
+
name: MTEB CMedQAv1
|
110 |
+
config: default
|
111 |
+
split: test
|
112 |
+
revision: None
|
113 |
+
metrics:
|
114 |
+
- type: map
|
115 |
+
value: 87.12213224673621
|
116 |
+
- type: mrr
|
117 |
+
value: 89.57150793650794
|
118 |
+
- task:
|
119 |
+
type: Reranking
|
120 |
+
dataset:
|
121 |
+
type: C-MTEB/CMedQAv2-reranking
|
122 |
+
name: MTEB CMedQAv2
|
123 |
+
config: default
|
124 |
+
split: test
|
125 |
+
revision: None
|
126 |
+
metrics:
|
127 |
+
- type: map
|
128 |
+
value: 87.57290061886421
|
129 |
+
- type: mrr
|
130 |
+
value: 90.19202380952382
|
131 |
+
- task:
|
132 |
+
type: Retrieval
|
133 |
+
dataset:
|
134 |
+
type: C-MTEB/CmedqaRetrieval
|
135 |
+
name: MTEB CmedqaRetrieval
|
136 |
+
config: default
|
137 |
+
split: dev
|
138 |
+
revision: None
|
139 |
+
metrics:
|
140 |
+
- type: map_at_1
|
141 |
+
value: 25.22
|
142 |
+
- type: map_at_10
|
143 |
+
value: 37.604
|
144 |
+
- type: map_at_100
|
145 |
+
value: 39.501
|
146 |
+
- type: map_at_1000
|
147 |
+
value: 39.614
|
148 |
+
- type: map_at_3
|
149 |
+
value: 33.378
|
150 |
+
- type: map_at_5
|
151 |
+
value: 35.774
|
152 |
+
- type: mrr_at_1
|
153 |
+
value: 38.385000000000005
|
154 |
+
- type: mrr_at_10
|
155 |
+
value: 46.487
|
156 |
+
- type: mrr_at_100
|
157 |
+
value: 47.504999999999995
|
158 |
+
- type: mrr_at_1000
|
159 |
+
value: 47.548
|
160 |
+
- type: mrr_at_3
|
161 |
+
value: 43.885999999999996
|
162 |
+
- type: mrr_at_5
|
163 |
+
value: 45.373000000000005
|
164 |
+
- type: ndcg_at_1
|
165 |
+
value: 38.385000000000005
|
166 |
+
- type: ndcg_at_10
|
167 |
+
value: 44.224999999999994
|
168 |
+
- type: ndcg_at_100
|
169 |
+
value: 51.637
|
170 |
+
- type: ndcg_at_1000
|
171 |
+
value: 53.55799999999999
|
172 |
+
- type: ndcg_at_3
|
173 |
+
value: 38.845
|
174 |
+
- type: ndcg_at_5
|
175 |
+
value: 41.163
|
176 |
+
- type: precision_at_1
|
177 |
+
value: 38.385000000000005
|
178 |
+
- type: precision_at_10
|
179 |
+
value: 9.812
|
180 |
+
- type: precision_at_100
|
181 |
+
value: 1.58
|
182 |
+
- type: precision_at_1000
|
183 |
+
value: 0.183
|
184 |
+
- type: precision_at_3
|
185 |
+
value: 21.88
|
186 |
+
- type: precision_at_5
|
187 |
+
value: 15.974
|
188 |
+
- type: recall_at_1
|
189 |
+
value: 25.22
|
190 |
+
- type: recall_at_10
|
191 |
+
value: 54.897
|
192 |
+
- type: recall_at_100
|
193 |
+
value: 85.469
|
194 |
+
- type: recall_at_1000
|
195 |
+
value: 98.18599999999999
|
196 |
+
- type: recall_at_3
|
197 |
+
value: 38.815
|
198 |
+
- type: recall_at_5
|
199 |
+
value: 45.885
|
200 |
+
- task:
|
201 |
+
type: PairClassification
|
202 |
+
dataset:
|
203 |
+
type: C-MTEB/CMNLI
|
204 |
+
name: MTEB Cmnli
|
205 |
+
config: default
|
206 |
+
split: validation
|
207 |
+
revision: None
|
208 |
+
metrics:
|
209 |
+
- type: cos_sim_accuracy
|
210 |
+
value: 83.22309079975948
|
211 |
+
- type: cos_sim_ap
|
212 |
+
value: 89.94833400328307
|
213 |
+
- type: cos_sim_f1
|
214 |
+
value: 84.39319055464031
|
215 |
+
- type: cos_sim_precision
|
216 |
+
value: 79.5774647887324
|
217 |
+
- type: cos_sim_recall
|
218 |
+
value: 89.82931961655366
|
219 |
+
- type: dot_accuracy
|
220 |
+
value: 83.22309079975948
|
221 |
+
- type: dot_ap
|
222 |
+
value: 89.95618559578415
|
223 |
+
- type: dot_f1
|
224 |
+
value: 84.41173239591345
|
225 |
+
- type: dot_precision
|
226 |
+
value: 79.61044343141317
|
227 |
+
- type: dot_recall
|
228 |
+
value: 89.82931961655366
|
229 |
+
- type: euclidean_accuracy
|
230 |
+
value: 83.23511725796753
|
231 |
+
- type: euclidean_ap
|
232 |
+
value: 89.94836342787318
|
233 |
+
- type: euclidean_f1
|
234 |
+
value: 84.40550133096718
|
235 |
+
- type: euclidean_precision
|
236 |
+
value: 80.29120067524794
|
237 |
+
- type: euclidean_recall
|
238 |
+
value: 88.9642272620996
|
239 |
+
- type: manhattan_accuracy
|
240 |
+
value: 83.23511725796753
|
241 |
+
- type: manhattan_ap
|
242 |
+
value: 89.9450103956978
|
243 |
+
- type: manhattan_f1
|
244 |
+
value: 84.44444444444444
|
245 |
+
- type: manhattan_precision
|
246 |
+
value: 80.09647651006712
|
247 |
+
- type: manhattan_recall
|
248 |
+
value: 89.29155950432546
|
249 |
+
- type: max_accuracy
|
250 |
+
value: 83.23511725796753
|
251 |
+
- type: max_ap
|
252 |
+
value: 89.95618559578415
|
253 |
+
- type: max_f1
|
254 |
+
value: 84.44444444444444
|
255 |
+
- task:
|
256 |
+
type: Retrieval
|
257 |
+
dataset:
|
258 |
+
type: C-MTEB/CovidRetrieval
|
259 |
+
name: MTEB CovidRetrieval
|
260 |
+
config: default
|
261 |
+
split: dev
|
262 |
+
revision: None
|
263 |
+
metrics:
|
264 |
+
- type: map_at_1
|
265 |
+
value: 76.87
|
266 |
+
- type: map_at_10
|
267 |
+
value: 84.502
|
268 |
+
- type: map_at_100
|
269 |
+
value: 84.615
|
270 |
+
- type: map_at_1000
|
271 |
+
value: 84.617
|
272 |
+
- type: map_at_3
|
273 |
+
value: 83.127
|
274 |
+
- type: map_at_5
|
275 |
+
value: 83.99600000000001
|
276 |
+
- type: mrr_at_1
|
277 |
+
value: 77.02799999999999
|
278 |
+
- type: mrr_at_10
|
279 |
+
value: 84.487
|
280 |
+
- type: mrr_at_100
|
281 |
+
value: 84.59299999999999
|
282 |
+
- type: mrr_at_1000
|
283 |
+
value: 84.59400000000001
|
284 |
+
- type: mrr_at_3
|
285 |
+
value: 83.193
|
286 |
+
- type: mrr_at_5
|
287 |
+
value: 83.994
|
288 |
+
- type: ndcg_at_1
|
289 |
+
value: 77.134
|
290 |
+
- type: ndcg_at_10
|
291 |
+
value: 87.68599999999999
|
292 |
+
- type: ndcg_at_100
|
293 |
+
value: 88.17099999999999
|
294 |
+
- type: ndcg_at_1000
|
295 |
+
value: 88.21
|
296 |
+
- type: ndcg_at_3
|
297 |
+
value: 84.993
|
298 |
+
- type: ndcg_at_5
|
299 |
+
value: 86.519
|
300 |
+
- type: precision_at_1
|
301 |
+
value: 77.134
|
302 |
+
- type: precision_at_10
|
303 |
+
value: 9.841999999999999
|
304 |
+
- type: precision_at_100
|
305 |
+
value: 1.006
|
306 |
+
- type: precision_at_1000
|
307 |
+
value: 0.101
|
308 |
+
- type: precision_at_3
|
309 |
+
value: 30.313000000000002
|
310 |
+
- type: precision_at_5
|
311 |
+
value: 18.945999999999998
|
312 |
+
- type: recall_at_1
|
313 |
+
value: 76.87
|
314 |
+
- type: recall_at_10
|
315 |
+
value: 97.418
|
316 |
+
- type: recall_at_100
|
317 |
+
value: 99.579
|
318 |
+
- type: recall_at_1000
|
319 |
+
value: 99.895
|
320 |
+
- type: recall_at_3
|
321 |
+
value: 90.227
|
322 |
+
- type: recall_at_5
|
323 |
+
value: 93.888
|
324 |
+
- task:
|
325 |
+
type: Retrieval
|
326 |
+
dataset:
|
327 |
+
type: C-MTEB/DuRetrieval
|
328 |
+
name: MTEB DuRetrieval
|
329 |
+
config: default
|
330 |
+
split: dev
|
331 |
+
revision: None
|
332 |
+
metrics:
|
333 |
+
- type: map_at_1
|
334 |
+
value: 25.941
|
335 |
+
- type: map_at_10
|
336 |
+
value: 78.793
|
337 |
+
- type: map_at_100
|
338 |
+
value: 81.57799999999999
|
339 |
+
- type: map_at_1000
|
340 |
+
value: 81.626
|
341 |
+
- type: map_at_3
|
342 |
+
value: 54.749
|
343 |
+
- type: map_at_5
|
344 |
+
value: 69.16
|
345 |
+
- type: mrr_at_1
|
346 |
+
value: 90.45
|
347 |
+
- type: mrr_at_10
|
348 |
+
value: 93.406
|
349 |
+
- type: mrr_at_100
|
350 |
+
value: 93.453
|
351 |
+
- type: mrr_at_1000
|
352 |
+
value: 93.45700000000001
|
353 |
+
- type: mrr_at_3
|
354 |
+
value: 93.10000000000001
|
355 |
+
- type: mrr_at_5
|
356 |
+
value: 93.27499999999999
|
357 |
+
- type: ndcg_at_1
|
358 |
+
value: 90.45
|
359 |
+
- type: ndcg_at_10
|
360 |
+
value: 86.44500000000001
|
361 |
+
- type: ndcg_at_100
|
362 |
+
value: 89.28399999999999
|
363 |
+
- type: ndcg_at_1000
|
364 |
+
value: 89.739
|
365 |
+
- type: ndcg_at_3
|
366 |
+
value: 85.62100000000001
|
367 |
+
- type: ndcg_at_5
|
368 |
+
value: 84.441
|
369 |
+
- type: precision_at_1
|
370 |
+
value: 90.45
|
371 |
+
- type: precision_at_10
|
372 |
+
value: 41.19
|
373 |
+
- type: precision_at_100
|
374 |
+
value: 4.761
|
375 |
+
- type: precision_at_1000
|
376 |
+
value: 0.48700000000000004
|
377 |
+
- type: precision_at_3
|
378 |
+
value: 76.583
|
379 |
+
- type: precision_at_5
|
380 |
+
value: 64.68
|
381 |
+
- type: recall_at_1
|
382 |
+
value: 25.941
|
383 |
+
- type: recall_at_10
|
384 |
+
value: 87.443
|
385 |
+
- type: recall_at_100
|
386 |
+
value: 96.54
|
387 |
+
- type: recall_at_1000
|
388 |
+
value: 98.906
|
389 |
+
- type: recall_at_3
|
390 |
+
value: 56.947
|
391 |
+
- type: recall_at_5
|
392 |
+
value: 73.714
|
393 |
+
- task:
|
394 |
+
type: Retrieval
|
395 |
+
dataset:
|
396 |
+
type: C-MTEB/EcomRetrieval
|
397 |
+
name: MTEB EcomRetrieval
|
398 |
+
config: default
|
399 |
+
split: dev
|
400 |
+
revision: None
|
401 |
+
metrics:
|
402 |
+
- type: map_at_1
|
403 |
+
value: 52.900000000000006
|
404 |
+
- type: map_at_10
|
405 |
+
value: 63.144
|
406 |
+
- type: map_at_100
|
407 |
+
value: 63.634
|
408 |
+
- type: map_at_1000
|
409 |
+
value: 63.644999999999996
|
410 |
+
- type: map_at_3
|
411 |
+
value: 60.817
|
412 |
+
- type: map_at_5
|
413 |
+
value: 62.202
|
414 |
+
- type: mrr_at_1
|
415 |
+
value: 52.900000000000006
|
416 |
+
- type: mrr_at_10
|
417 |
+
value: 63.144
|
418 |
+
- type: mrr_at_100
|
419 |
+
value: 63.634
|
420 |
+
- type: mrr_at_1000
|
421 |
+
value: 63.644999999999996
|
422 |
+
- type: mrr_at_3
|
423 |
+
value: 60.817
|
424 |
+
- type: mrr_at_5
|
425 |
+
value: 62.202
|
426 |
+
- type: ndcg_at_1
|
427 |
+
value: 52.900000000000006
|
428 |
+
- type: ndcg_at_10
|
429 |
+
value: 68.042
|
430 |
+
- type: ndcg_at_100
|
431 |
+
value: 70.417
|
432 |
+
- type: ndcg_at_1000
|
433 |
+
value: 70.722
|
434 |
+
- type: ndcg_at_3
|
435 |
+
value: 63.287000000000006
|
436 |
+
- type: ndcg_at_5
|
437 |
+
value: 65.77
|
438 |
+
- type: precision_at_1
|
439 |
+
value: 52.900000000000006
|
440 |
+
- type: precision_at_10
|
441 |
+
value: 8.34
|
442 |
+
- type: precision_at_100
|
443 |
+
value: 0.9450000000000001
|
444 |
+
- type: precision_at_1000
|
445 |
+
value: 0.097
|
446 |
+
- type: precision_at_3
|
447 |
+
value: 23.467
|
448 |
+
- type: precision_at_5
|
449 |
+
value: 15.28
|
450 |
+
- type: recall_at_1
|
451 |
+
value: 52.900000000000006
|
452 |
+
- type: recall_at_10
|
453 |
+
value: 83.39999999999999
|
454 |
+
- type: recall_at_100
|
455 |
+
value: 94.5
|
456 |
+
- type: recall_at_1000
|
457 |
+
value: 96.89999999999999
|
458 |
+
- type: recall_at_3
|
459 |
+
value: 70.39999999999999
|
460 |
+
- type: recall_at_5
|
461 |
+
value: 76.4
|
462 |
+
- task:
|
463 |
+
type: Classification
|
464 |
+
dataset:
|
465 |
+
type: C-MTEB/IFlyTek-classification
|
466 |
+
name: MTEB IFlyTek
|
467 |
+
config: default
|
468 |
+
split: validation
|
469 |
+
revision: None
|
470 |
+
metrics:
|
471 |
+
- type: accuracy
|
472 |
+
value: 49.74220854174683
|
473 |
+
- type: f1
|
474 |
+
value: 38.01399980618159
|
475 |
+
- task:
|
476 |
+
type: Classification
|
477 |
+
dataset:
|
478 |
+
type: C-MTEB/JDReview-classification
|
479 |
+
name: MTEB JDReview
|
480 |
+
config: default
|
481 |
+
split: test
|
482 |
+
revision: None
|
483 |
+
metrics:
|
484 |
+
- type: accuracy
|
485 |
+
value: 86.73545966228893
|
486 |
+
- type: ap
|
487 |
+
value: 55.72394235169542
|
488 |
+
- type: f1
|
489 |
+
value: 81.58550390953492
|
490 |
+
- task:
|
491 |
+
type: STS
|
492 |
+
dataset:
|
493 |
+
type: C-MTEB/LCQMC
|
494 |
+
name: MTEB LCQMC
|
495 |
+
config: default
|
496 |
+
split: test
|
497 |
+
revision: None
|
498 |
+
metrics:
|
499 |
+
- type: cos_sim_pearson
|
500 |
+
value: 69.96711977441642
|
501 |
+
- type: cos_sim_spearman
|
502 |
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value: 75.54747609685569
|
503 |
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- type: euclidean_pearson
|
504 |
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value: 74.62663478056035
|
505 |
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- type: euclidean_spearman
|
506 |
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value: 75.54761576699639
|
507 |
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- type: manhattan_pearson
|
508 |
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value: 74.60983904582241
|
509 |
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- type: manhattan_spearman
|
510 |
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value: 75.52758938061503
|
511 |
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- task:
|
512 |
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type: Reranking
|
513 |
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dataset:
|
514 |
+
type: C-MTEB/Mmarco-reranking
|
515 |
+
name: MTEB MMarcoReranking
|
516 |
+
config: default
|
517 |
+
split: dev
|
518 |
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revision: None
|
519 |
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metrics:
|
520 |
+
- type: map
|
521 |
+
value: 28.076927649720986
|
522 |
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- type: mrr
|
523 |
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value: 26.98015873015873
|
524 |
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- task:
|
525 |
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type: Retrieval
|
526 |
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dataset:
|
527 |
+
type: C-MTEB/MMarcoRetrieval
|
528 |
+
name: MTEB MMarcoRetrieval
|
529 |
+
config: default
|
530 |
+
split: dev
|
531 |
+
revision: None
|
532 |
+
metrics:
|
533 |
+
- type: map_at_1
|
534 |
+
value: 65.58
|
535 |
+
- type: map_at_10
|
536 |
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value: 74.763
|
537 |
+
- type: map_at_100
|
538 |
+
value: 75.077
|
539 |
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- type: map_at_1000
|
540 |
+
value: 75.091
|
541 |
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- type: map_at_3
|
542 |
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value: 72.982
|
543 |
+
- type: map_at_5
|
544 |
+
value: 74.155
|
545 |
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- type: mrr_at_1
|
546 |
+
value: 67.822
|
547 |
+
- type: mrr_at_10
|
548 |
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value: 75.437
|
549 |
+
- type: mrr_at_100
|
550 |
+
value: 75.702
|
551 |
+
- type: mrr_at_1000
|
552 |
+
value: 75.715
|
553 |
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- type: mrr_at_3
|
554 |
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value: 73.91799999999999
|
555 |
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- type: mrr_at_5
|
556 |
+
value: 74.909
|
557 |
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- type: ndcg_at_1
|
558 |
+
value: 67.822
|
559 |
+
- type: ndcg_at_10
|
560 |
+
value: 78.472
|
561 |
+
- type: ndcg_at_100
|
562 |
+
value: 79.891
|
563 |
+
- type: ndcg_at_1000
|
564 |
+
value: 80.262
|
565 |
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- type: ndcg_at_3
|
566 |
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value: 75.138
|
567 |
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- type: ndcg_at_5
|
568 |
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value: 77.094
|
569 |
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- type: precision_at_1
|
570 |
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value: 67.822
|
571 |
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- type: precision_at_10
|
572 |
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value: 9.474
|
573 |
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- type: precision_at_100
|
574 |
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value: 1.019
|
575 |
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- type: precision_at_1000
|
576 |
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value: 0.105
|
577 |
+
- type: precision_at_3
|
578 |
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value: 28.281
|
579 |
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- type: precision_at_5
|
580 |
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value: 18.017
|
581 |
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- type: recall_at_1
|
582 |
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value: 65.58
|
583 |
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- type: recall_at_10
|
584 |
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value: 89.18599999999999
|
585 |
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- type: recall_at_100
|
586 |
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value: 95.64399999999999
|
587 |
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- type: recall_at_1000
|
588 |
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value: 98.541
|
589 |
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- type: recall_at_3
|
590 |
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value: 80.455
|
591 |
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- type: recall_at_5
|
592 |
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value: 85.063
|
593 |
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- task:
|
594 |
+
type: Classification
|
595 |
+
dataset:
|
596 |
+
type: mteb/amazon_massive_intent
|
597 |
+
name: MTEB MassiveIntentClassification (zh-CN)
|
598 |
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config: zh-CN
|
599 |
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split: test
|
600 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
601 |
+
metrics:
|
602 |
+
- type: accuracy
|
603 |
+
value: 72.86819098856758
|
604 |
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- type: f1
|
605 |
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value: 70.25369778283451
|
606 |
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- task:
|
607 |
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type: Classification
|
608 |
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dataset:
|
609 |
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type: mteb/amazon_massive_scenario
|
610 |
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name: MTEB MassiveScenarioClassification (zh-CN)
|
611 |
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config: zh-CN
|
612 |
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split: test
|
613 |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
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614 |
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metrics:
|
615 |
+
- type: accuracy
|
616 |
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value: 75.46738399462004
|
617 |
+
- type: f1
|
618 |
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value: 75.02466838130249
|
619 |
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- task:
|
620 |
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type: Retrieval
|
621 |
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dataset:
|
622 |
+
type: C-MTEB/MedicalRetrieval
|
623 |
+
name: MTEB MedicalRetrieval
|
624 |
+
config: default
|
625 |
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split: dev
|
626 |
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revision: None
|
627 |
+
metrics:
|
628 |
+
- type: map_at_1
|
629 |
+
value: 53.300000000000004
|
630 |
+
- type: map_at_10
|
631 |
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value: 60.072
|
632 |
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- type: map_at_100
|
633 |
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value: 60.618
|
634 |
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- type: map_at_1000
|
635 |
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value: 60.659
|
636 |
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- type: map_at_3
|
637 |
+
value: 58.550000000000004
|
638 |
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- type: map_at_5
|
639 |
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value: 59.425
|
640 |
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- type: mrr_at_1
|
641 |
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value: 53.5
|
642 |
+
- type: mrr_at_10
|
643 |
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value: 60.187999999999995
|
644 |
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- type: mrr_at_100
|
645 |
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value: 60.73499999999999
|
646 |
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- type: mrr_at_1000
|
647 |
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value: 60.775999999999996
|
648 |
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- type: mrr_at_3
|
649 |
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value: 58.667
|
650 |
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- type: mrr_at_5
|
651 |
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value: 59.541999999999994
|
652 |
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- type: ndcg_at_1
|
653 |
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value: 53.300000000000004
|
654 |
+
- type: ndcg_at_10
|
655 |
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value: 63.376999999999995
|
656 |
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- type: ndcg_at_100
|
657 |
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value: 66.24600000000001
|
658 |
+
- type: ndcg_at_1000
|
659 |
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value: 67.408
|
660 |
+
- type: ndcg_at_3
|
661 |
+
value: 60.211000000000006
|
662 |
+
- type: ndcg_at_5
|
663 |
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value: 61.781
|
664 |
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- type: precision_at_1
|
665 |
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value: 53.300000000000004
|
666 |
+
- type: precision_at_10
|
667 |
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value: 7.380000000000001
|
668 |
+
- type: precision_at_100
|
669 |
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value: 0.877
|
670 |
+
- type: precision_at_1000
|
671 |
+
value: 0.097
|
672 |
+
- type: precision_at_3
|
673 |
+
value: 21.667
|
674 |
+
- type: precision_at_5
|
675 |
+
value: 13.76
|
676 |
+
- type: recall_at_1
|
677 |
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value: 53.300000000000004
|
678 |
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- type: recall_at_10
|
679 |
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value: 73.8
|
680 |
+
- type: recall_at_100
|
681 |
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value: 87.7
|
682 |
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- type: recall_at_1000
|
683 |
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value: 97.0
|
684 |
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- type: recall_at_3
|
685 |
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value: 65.0
|
686 |
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- type: recall_at_5
|
687 |
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value: 68.8
|
688 |
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- task:
|
689 |
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type: Classification
|
690 |
+
dataset:
|
691 |
+
type: C-MTEB/MultilingualSentiment-classification
|
692 |
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name: MTEB MultilingualSentiment
|
693 |
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config: default
|
694 |
+
split: validation
|
695 |
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revision: None
|
696 |
+
metrics:
|
697 |
+
- type: accuracy
|
698 |
+
value: 76.27666666666667
|
699 |
+
- type: f1
|
700 |
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value: 76.31280038435165
|
701 |
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- task:
|
702 |
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type: PairClassification
|
703 |
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dataset:
|
704 |
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type: C-MTEB/OCNLI
|
705 |
+
name: MTEB Ocnli
|
706 |
+
config: default
|
707 |
+
split: validation
|
708 |
+
revision: None
|
709 |
+
metrics:
|
710 |
+
- type: cos_sim_accuracy
|
711 |
+
value: 78.72225230102869
|
712 |
+
- type: cos_sim_ap
|
713 |
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value: 80.63941899467723
|
714 |
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- type: cos_sim_f1
|
715 |
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value: 80.52190121155638
|
716 |
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- type: cos_sim_precision
|
717 |
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value: 72.06005004170142
|
718 |
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- type: cos_sim_recall
|
719 |
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value: 91.23548046462513
|
720 |
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- type: dot_accuracy
|
721 |
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value: 78.72225230102869
|
722 |
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- type: dot_ap
|
723 |
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value: 80.63913939812744
|
724 |
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- type: dot_f1
|
725 |
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value: 80.51948051948052
|
726 |
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- type: dot_precision
|
727 |
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value: 71.7948717948718
|
728 |
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- type: dot_recall
|
729 |
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value: 91.65786694825766
|
730 |
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- type: euclidean_accuracy
|
731 |
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value: 78.72225230102869
|
732 |
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- type: euclidean_ap
|
733 |
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value: 80.64403797436798
|
734 |
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- type: euclidean_f1
|
735 |
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value: 80.52190121155638
|
736 |
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- type: euclidean_precision
|
737 |
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value: 72.06005004170142
|
738 |
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- type: euclidean_recall
|
739 |
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value: 91.23548046462513
|
740 |
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- type: manhattan_accuracy
|
741 |
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value: 78.18083378451544
|
742 |
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- type: manhattan_ap
|
743 |
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value: 80.5241189302444
|
744 |
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- type: manhattan_f1
|
745 |
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value: 80.43478260869566
|
746 |
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- type: manhattan_precision
|
747 |
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value: 72.7972626176219
|
748 |
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- type: manhattan_recall
|
749 |
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value: 89.86272439281943
|
750 |
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- type: max_accuracy
|
751 |
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value: 78.72225230102869
|
752 |
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- type: max_ap
|
753 |
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value: 80.64403797436798
|
754 |
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- type: max_f1
|
755 |
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value: 80.52190121155638
|
756 |
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- task:
|
757 |
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type: Classification
|
758 |
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dataset:
|
759 |
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type: C-MTEB/OnlineShopping-classification
|
760 |
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name: MTEB OnlineShopping
|
761 |
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config: default
|
762 |
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split: test
|
763 |
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revision: None
|
764 |
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metrics:
|
765 |
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- type: accuracy
|
766 |
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value: 92.49000000000001
|
767 |
+
- type: ap
|
768 |
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value: 90.66330807324402
|
769 |
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- type: f1
|
770 |
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value: 92.48245049107115
|
771 |
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- task:
|
772 |
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type: STS
|
773 |
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dataset:
|
774 |
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type: C-MTEB/PAWSX
|
775 |
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name: MTEB PAWSX
|
776 |
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config: default
|
777 |
+
split: test
|
778 |
+
revision: None
|
779 |
+
metrics:
|
780 |
+
- type: cos_sim_pearson
|
781 |
+
value: 33.6275431596535
|
782 |
+
- type: cos_sim_spearman
|
783 |
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value: 37.865700050451494
|
784 |
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- type: euclidean_pearson
|
785 |
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value: 38.1050665279388
|
786 |
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- type: euclidean_spearman
|
787 |
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value: 37.864125056066364
|
788 |
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- type: manhattan_pearson
|
789 |
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value: 38.11206873232881
|
790 |
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- type: manhattan_spearman
|
791 |
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value: 37.852977098473936
|
792 |
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- task:
|
793 |
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type: STS
|
794 |
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dataset:
|
795 |
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type: C-MTEB/QBQTC
|
796 |
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name: MTEB QBQTC
|
797 |
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config: default
|
798 |
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split: test
|
799 |
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revision: None
|
800 |
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metrics:
|
801 |
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- type: cos_sim_pearson
|
802 |
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value: 32.137955501231104
|
803 |
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- type: cos_sim_spearman
|
804 |
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value: 33.68610910423116
|
805 |
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- type: euclidean_pearson
|
806 |
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value: 32.155444753547926
|
807 |
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- type: euclidean_spearman
|
808 |
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value: 33.685799252964124
|
809 |
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- type: manhattan_pearson
|
810 |
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value: 32.14490855334317
|
811 |
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- type: manhattan_spearman
|
812 |
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value: 33.656549820048554
|
813 |
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- task:
|
814 |
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type: STS
|
815 |
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dataset:
|
816 |
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type: mteb/sts22-crosslingual-sts
|
817 |
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name: MTEB STS22 (zh)
|
818 |
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config: zh
|
819 |
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split: test
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820 |
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revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
821 |
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metrics:
|
822 |
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- type: cos_sim_pearson
|
823 |
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value: 63.63884916818661
|
824 |
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- type: cos_sim_spearman
|
825 |
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value: 64.3217581571435
|
826 |
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- type: euclidean_pearson
|
827 |
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value: 63.475760085926055
|
828 |
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- type: euclidean_spearman
|
829 |
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value: 64.31638169371887
|
830 |
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- type: manhattan_pearson
|
831 |
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value: 64.39677572604752
|
832 |
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- type: manhattan_spearman
|
833 |
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value: 64.85585019406021
|
834 |
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- task:
|
835 |
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type: STS
|
836 |
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dataset:
|
837 |
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type: C-MTEB/STSB
|
838 |
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name: MTEB STSB
|
839 |
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config: default
|
840 |
+
split: test
|
841 |
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revision: None
|
842 |
+
metrics:
|
843 |
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- type: cos_sim_pearson
|
844 |
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value: 79.74698333415277
|
845 |
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- type: cos_sim_spearman
|
846 |
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value: 81.1850043859317
|
847 |
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- type: euclidean_pearson
|
848 |
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value: 80.94512578669881
|
849 |
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- type: euclidean_spearman
|
850 |
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value: 81.18825478390181
|
851 |
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- type: manhattan_pearson
|
852 |
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value: 80.88114336824758
|
853 |
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- type: manhattan_spearman
|
854 |
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value: 81.12266715583868
|
855 |
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- task:
|
856 |
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type: Reranking
|
857 |
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dataset:
|
858 |
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type: C-MTEB/T2Reranking
|
859 |
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name: MTEB T2Reranking
|
860 |
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config: default
|
861 |
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split: dev
|
862 |
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revision: None
|
863 |
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metrics:
|
864 |
+
- type: map
|
865 |
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value: 66.59971552953814
|
866 |
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- type: mrr
|
867 |
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value: 76.42177408088038
|
868 |
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- task:
|
869 |
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type: Retrieval
|
870 |
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dataset:
|
871 |
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type: C-MTEB/T2Retrieval
|
872 |
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name: MTEB T2Retrieval
|
873 |
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config: default
|
874 |
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split: dev
|
875 |
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revision: None
|
876 |
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metrics:
|
877 |
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- type: map_at_1
|
878 |
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value: 28.825
|
879 |
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- type: map_at_10
|
880 |
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value: 77.48899999999999
|
881 |
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- type: map_at_100
|
882 |
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value: 81.144
|
883 |
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- type: map_at_1000
|
884 |
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value: 81.216
|
885 |
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- type: map_at_3
|
886 |
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value: 55.435
|
887 |
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- type: map_at_5
|
888 |
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value: 67.496
|
889 |
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- type: mrr_at_1
|
890 |
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value: 91.377
|
891 |
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- type: mrr_at_10
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892 |
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value: 94.062
|
893 |
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- type: mrr_at_100
|
894 |
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value: 94.122
|
895 |
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- type: mrr_at_1000
|
896 |
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value: 94.123
|
897 |
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- type: mrr_at_3
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898 |
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value: 93.709
|
899 |
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- type: mrr_at_5
|
900 |
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value: 93.932
|
901 |
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- type: ndcg_at_1
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902 |
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value: 91.377
|
903 |
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- type: ndcg_at_10
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904 |
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value: 85.44800000000001
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905 |
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- type: ndcg_at_100
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906 |
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value: 89.11099999999999
|
907 |
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- type: ndcg_at_1000
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908 |
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value: 89.752
|
909 |
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- type: ndcg_at_3
|
910 |
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value: 87.262
|
911 |
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- type: ndcg_at_5
|
912 |
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value: 85.668
|
913 |
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- type: precision_at_1
|
914 |
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value: 91.377
|
915 |
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- type: precision_at_10
|
916 |
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value: 41.525
|
917 |
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- type: precision_at_100
|
918 |
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value: 4.989
|
919 |
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- type: precision_at_1000
|
920 |
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value: 0.516
|
921 |
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- type: precision_at_3
|
922 |
+
value: 75.452
|
923 |
+
- type: precision_at_5
|
924 |
+
value: 62.785000000000004
|
925 |
+
- type: recall_at_1
|
926 |
+
value: 28.825
|
927 |
+
- type: recall_at_10
|
928 |
+
value: 84.202
|
929 |
+
- type: recall_at_100
|
930 |
+
value: 95.768
|
931 |
+
- type: recall_at_1000
|
932 |
+
value: 98.791
|
933 |
+
- type: recall_at_3
|
934 |
+
value: 57.284
|
935 |
+
- type: recall_at_5
|
936 |
+
value: 71.071
|
937 |
+
- task:
|
938 |
+
type: Classification
|
939 |
+
dataset:
|
940 |
+
type: C-MTEB/TNews-classification
|
941 |
+
name: MTEB TNews
|
942 |
+
config: default
|
943 |
+
split: validation
|
944 |
+
revision: None
|
945 |
+
metrics:
|
946 |
+
- type: accuracy
|
947 |
+
value: 52.160000000000004
|
948 |
+
- type: f1
|
949 |
+
value: 50.49492950548829
|
950 |
+
- task:
|
951 |
+
type: Clustering
|
952 |
+
dataset:
|
953 |
+
type: C-MTEB/ThuNewsClusteringP2P
|
954 |
+
name: MTEB ThuNewsClusteringP2P
|
955 |
+
config: default
|
956 |
+
split: test
|
957 |
+
revision: None
|
958 |
+
metrics:
|
959 |
+
- type: v_measure
|
960 |
+
value: 70.06019845009966
|
961 |
+
- task:
|
962 |
+
type: Clustering
|
963 |
+
dataset:
|
964 |
+
type: C-MTEB/ThuNewsClusteringS2S
|
965 |
+
name: MTEB ThuNewsClusteringS2S
|
966 |
+
config: default
|
967 |
+
split: test
|
968 |
+
revision: None
|
969 |
+
metrics:
|
970 |
+
- type: v_measure
|
971 |
+
value: 63.9370959228245
|
972 |
+
- task:
|
973 |
+
type: Retrieval
|
974 |
+
dataset:
|
975 |
+
type: C-MTEB/VideoRetrieval
|
976 |
+
name: MTEB VideoRetrieval
|
977 |
+
config: default
|
978 |
+
split: dev
|
979 |
+
revision: None
|
980 |
+
metrics:
|
981 |
+
- type: map_at_1
|
982 |
+
value: 60.0
|
983 |
+
- type: map_at_10
|
984 |
+
value: 69.362
|
985 |
+
- type: map_at_100
|
986 |
+
value: 69.819
|
987 |
+
- type: map_at_1000
|
988 |
+
value: 69.833
|
989 |
+
- type: map_at_3
|
990 |
+
value: 67.783
|
991 |
+
- type: map_at_5
|
992 |
+
value: 68.71300000000001
|
993 |
+
- type: mrr_at_1
|
994 |
+
value: 60.0
|
995 |
+
- type: mrr_at_10
|
996 |
+
value: 69.362
|
997 |
+
- type: mrr_at_100
|
998 |
+
value: 69.819
|
999 |
+
- type: mrr_at_1000
|
1000 |
+
value: 69.833
|
1001 |
+
- type: mrr_at_3
|
1002 |
+
value: 67.783
|
1003 |
+
- type: mrr_at_5
|
1004 |
+
value: 68.71300000000001
|
1005 |
+
- type: ndcg_at_1
|
1006 |
+
value: 60.0
|
1007 |
+
- type: ndcg_at_10
|
1008 |
+
value: 73.59400000000001
|
1009 |
+
- type: ndcg_at_100
|
1010 |
+
value: 75.734
|
1011 |
+
- type: ndcg_at_1000
|
1012 |
+
value: 76.049
|
1013 |
+
- type: ndcg_at_3
|
1014 |
+
value: 70.33
|
1015 |
+
- type: ndcg_at_5
|
1016 |
+
value: 72.033
|
1017 |
+
- type: precision_at_1
|
1018 |
+
value: 60.0
|
1019 |
+
- type: precision_at_10
|
1020 |
+
value: 8.67
|
1021 |
+
- type: precision_at_100
|
1022 |
+
value: 0.9650000000000001
|
1023 |
+
- type: precision_at_1000
|
1024 |
+
value: 0.099
|
1025 |
+
- type: precision_at_3
|
1026 |
+
value: 25.900000000000002
|
1027 |
+
- type: precision_at_5
|
1028 |
+
value: 16.38
|
1029 |
+
- type: recall_at_1
|
1030 |
+
value: 60.0
|
1031 |
+
- type: recall_at_10
|
1032 |
+
value: 86.7
|
1033 |
+
- type: recall_at_100
|
1034 |
+
value: 96.5
|
1035 |
+
- type: recall_at_1000
|
1036 |
+
value: 98.9
|
1037 |
+
- type: recall_at_3
|
1038 |
+
value: 77.7
|
1039 |
+
- type: recall_at_5
|
1040 |
+
value: 81.89999999999999
|
1041 |
+
- task:
|
1042 |
+
type: Classification
|
1043 |
+
dataset:
|
1044 |
+
type: C-MTEB/waimai-classification
|
1045 |
+
name: MTEB Waimai
|
1046 |
+
config: default
|
1047 |
+
split: test
|
1048 |
+
revision: None
|
1049 |
+
metrics:
|
1050 |
+
- type: accuracy
|
1051 |
+
value: 88.36
|
1052 |
+
- type: ap
|
1053 |
+
value: 73.25144216855439
|
1054 |
+
- type: f1
|
1055 |
+
value: 86.75076261442027
|
1056 |
---
|
1057 |
+
# xiaobu-embedding
|
1058 |
+
|
1059 |
+
模型:基于GTE模型[1]多任务微调。
|
1060 |
+
数据:闲聊类Query-Query、知识类Query-Doc、BGE开源Query-Doc[2];清洗正例,挖掘中等难度负例;累计6M(质量更重要)。
|
1061 |
+
|
1062 |
+
## Usage (Sentence-Transformers)
|
1063 |
+
|
1064 |
+
```
|
1065 |
+
pip install -U sentence-transformers
|
1066 |
+
```
|
1067 |
+
相似度计算:
|
1068 |
+
```python
|
1069 |
+
from sentence_transformers import SentenceTransformer
|
1070 |
+
sentences_1 = ["样例数据-1", "样例数据-2"]
|
1071 |
+
sentences_2 = ["样例数据-3", "样例数据-4"]
|
1072 |
+
model = SentenceTransformer('lier007/xiaobu-embedding')
|
1073 |
+
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
|
1074 |
+
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
|
1075 |
+
similarity = embeddings_1 @ embeddings_2.T
|
1076 |
+
print(similarity)
|
1077 |
+
```
|
1078 |
+
|
1079 |
+
## Evaluation
|
1080 |
+
参考BGE中文CMTEB评估[2]
|
1081 |
+
|
1082 |
+
## Finetune
|
1083 |
+
参考BGE微调模块[2]
|
1084 |
+
|
1085 |
+
## Reference
|
1086 |
+
1. https://huggingface.co/thenlper/gte-large-zh
|
1087 |
+
2. https://github.com/FlagOpen/FlagEmbedding
|
added_tokens.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"[CLS]": 101,
|
3 |
+
"[MASK]": 103,
|
4 |
+
"[PAD]": 0,
|
5 |
+
"[SEP]": 102,
|
6 |
+
"[UNK]": 100
|
7 |
+
}
|
config.json
ADDED
@@ -0,0 +1,31 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"BertModel"
|
4 |
+
],
|
5 |
+
"attention_probs_dropout_prob": 0.1,
|
6 |
+
"classifier_dropout": null,
|
7 |
+
"directionality": "bidi",
|
8 |
+
"gradient_checkpointing": false,
|
9 |
+
"hidden_act": "gelu",
|
10 |
+
"hidden_dropout_prob": 0.1,
|
11 |
+
"hidden_size": 1024,
|
12 |
+
"initializer_range": 0.02,
|
13 |
+
"intermediate_size": 4096,
|
14 |
+
"layer_norm_eps": 1e-12,
|
15 |
+
"max_position_embeddings": 512,
|
16 |
+
"model_type": "bert",
|
17 |
+
"num_attention_heads": 16,
|
18 |
+
"num_hidden_layers": 24,
|
19 |
+
"pad_token_id": 0,
|
20 |
+
"pooler_fc_size": 768,
|
21 |
+
"pooler_num_attention_heads": 12,
|
22 |
+
"pooler_num_fc_layers": 3,
|
23 |
+
"pooler_size_per_head": 128,
|
24 |
+
"pooler_type": "first_token_transform",
|
25 |
+
"position_embedding_type": "absolute",
|
26 |
+
"torch_dtype": "float32",
|
27 |
+
"transformers_version": "4.34.0",
|
28 |
+
"type_vocab_size": 2,
|
29 |
+
"use_cache": true,
|
30 |
+
"vocab_size": 21128
|
31 |
+
}
|
config_sentence_transformers.json
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"__version__": {
|
3 |
+
"sentence_transformers": "2.2.2",
|
4 |
+
"transformers": "4.34.0",
|
5 |
+
"pytorch": "2.0.1+cu118"
|
6 |
+
}
|
7 |
+
}
|
modules.json
ADDED
@@ -0,0 +1,20 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[
|
2 |
+
{
|
3 |
+
"idx": 0,
|
4 |
+
"name": "0",
|
5 |
+
"path": "",
|
6 |
+
"type": "sentence_transformers.models.Transformer"
|
7 |
+
},
|
8 |
+
{
|
9 |
+
"idx": 1,
|
10 |
+
"name": "1",
|
11 |
+
"path": "1_Pooling",
|
12 |
+
"type": "sentence_transformers.models.Pooling"
|
13 |
+
},
|
14 |
+
{
|
15 |
+
"idx": 2,
|
16 |
+
"name": "2",
|
17 |
+
"path": "2_Normalize",
|
18 |
+
"type": "sentence_transformers.models.Normalize"
|
19 |
+
}
|
20 |
+
]
|
pytorch_model.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8fd1e81e069dadb0469f24dd048b5778e6ab9d3ac79253514550ae0b1125bb08
|
3 |
+
size 1302216105
|
sentence_bert_config.json
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"max_seq_length": 512,
|
3 |
+
"do_lower_case": false
|
4 |
+
}
|
special_tokens_map.json
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"additional_special_tokens": [
|
3 |
+
"[PAD]",
|
4 |
+
"[UNK]",
|
5 |
+
"[CLS]",
|
6 |
+
"[SEP]",
|
7 |
+
"[MASK]"
|
8 |
+
],
|
9 |
+
"cls_token": "[CLS]",
|
10 |
+
"mask_token": "[MASK]",
|
11 |
+
"pad_token": "[PAD]",
|
12 |
+
"sep_token": "[SEP]",
|
13 |
+
"unk_token": "[UNK]"
|
14 |
+
}
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"0": {
|
4 |
+
"content": "[PAD]",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"100": {
|
12 |
+
"content": "[UNK]",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"101": {
|
20 |
+
"content": "[CLS]",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"102": {
|
28 |
+
"content": "[SEP]",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"103": {
|
36 |
+
"content": "[MASK]",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
}
|
43 |
+
},
|
44 |
+
"additional_special_tokens": [
|
45 |
+
"[PAD]",
|
46 |
+
"[UNK]",
|
47 |
+
"[CLS]",
|
48 |
+
"[SEP]",
|
49 |
+
"[MASK]"
|
50 |
+
],
|
51 |
+
"clean_up_tokenization_spaces": true,
|
52 |
+
"cls_token": "[CLS]",
|
53 |
+
"do_basic_tokenize": true,
|
54 |
+
"do_lower_case": true,
|
55 |
+
"mask_token": "[MASK]",
|
56 |
+
"max_length": 256,
|
57 |
+
"model_max_length": 1000000000000000019884624838656,
|
58 |
+
"never_split": null,
|
59 |
+
"pad_to_multiple_of": null,
|
60 |
+
"pad_token": "[PAD]",
|
61 |
+
"pad_token_type_id": 0,
|
62 |
+
"padding_side": "right",
|
63 |
+
"sep_token": "[SEP]",
|
64 |
+
"stride": 0,
|
65 |
+
"strip_accents": null,
|
66 |
+
"tokenize_chinese_chars": true,
|
67 |
+
"tokenizer_class": "BertTokenizer",
|
68 |
+
"truncation_side": "right",
|
69 |
+
"truncation_strategy": "longest_first",
|
70 |
+
"unk_token": "[UNK]"
|
71 |
+
}
|
vocab.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|