Hugging Face's logo

Datasets:
stefan-it
/
autotrain-flair-hipe2022-fr-hmbert

Dataset card Files Community
stefan-it commited on
Commit
9d649f4
•
1 Parent(s): c81dfda

Upload folder using huggingface_hub

Browse files
Files changed (6) hide show
  1. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/best-model.pt +3 -0
  2. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/dev.tsv +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/final-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/loss.tsv +11 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/test.tsv +0 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/training.log +245 -0
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/best-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b40896df61adddfe57cfc4a53d91ebb1a34ca2f2a73cad6b64e81795f51bfbf3
3
+ size 443334288
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/dev.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/final-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f352f546ed806b5eb91fc293a85b055ca51ec6a0723bd22b0ebe781cf7acbe93
3
+ size 443334491
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/loss.tsv ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ EPOCH TIMESTAMP LEARNING_RATE TRAIN_LOSS DEV_LOSS DEV_PRECISION DEV_RECALL DEV_F1 DEV_ACCURACY
2
+ 1 13:41:36 0.0000 0.4968 0.1471 0.6404 0.7171 0.6766 0.5362
3
+ 2 13:44:14 0.0000 0.1220 0.1313 0.7268 0.8047 0.7638 0.6439
4
+ 3 13:46:53 0.0000 0.0744 0.1363 0.7903 0.7835 0.7869 0.6749
5
+ 4 13:49:32 0.0000 0.0497 0.1498 0.8081 0.7984 0.8032 0.6987
6
+ 5 13:52:11 0.0000 0.0370 0.1908 0.7692 0.8190 0.7933 0.6895
7
+ 6 13:54:49 0.0000 0.0258 0.1992 0.7866 0.8276 0.8066 0.7045
8
+ 7 13:57:28 0.0000 0.0184 0.2073 0.8067 0.8316 0.8190 0.7188
9
+ 8 14:00:05 0.0000 0.0121 0.2242 0.8055 0.8373 0.8211 0.7234
10
+ 9 14:02:44 0.0000 0.0089 0.2307 0.8168 0.8276 0.8222 0.7265
11
+ 10 14:05:24 0.0000 0.0052 0.2313 0.8058 0.8270 0.8163 0.7184
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3/training.log ADDED
@@ -0,0 +1,245 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-09-04 13:39:01,977 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-04 13:39:01,978 Model: "SequenceTagger(
3
+ (embeddings): TransformerWordEmbeddings(
4
+ (model): BertModel(
5
+ (embeddings): BertEmbeddings(
6
+ (word_embeddings): Embedding(32001, 768)
7
+ (position_embeddings): Embedding(512, 768)
8
+ (token_type_embeddings): Embedding(2, 768)
9
+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
10
+ (dropout): Dropout(p=0.1, inplace=False)
11
+ )
12
+ (encoder): BertEncoder(
13
+ (layer): ModuleList(
14
+ (0-11): 12 x BertLayer(
15
+ (attention): BertAttention(
16
+ (self): BertSelfAttention(
17
+ (query): Linear(in_features=768, out_features=768, bias=True)
18
+ (key): Linear(in_features=768, out_features=768, bias=True)
19
+ (value): Linear(in_features=768, out_features=768, bias=True)
20
+ (dropout): Dropout(p=0.1, inplace=False)
21
+ )
22
+ (output): BertSelfOutput(
23
+ (dense): Linear(in_features=768, out_features=768, bias=True)
24
+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
25
+ (dropout): Dropout(p=0.1, inplace=False)
26
+ )
27
+ )
28
+ (intermediate): BertIntermediate(
29
+ (dense): Linear(in_features=768, out_features=3072, bias=True)
30
+ (intermediate_act_fn): GELUActivation()
31
+ )
32
+ (output): BertOutput(
33
+ (dense): Linear(in_features=3072, out_features=768, bias=True)
34
+ (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
35
+ (dropout): Dropout(p=0.1, inplace=False)
36
+ )
37
+ )
38
+ )
39
+ )
40
+ (pooler): BertPooler(
41
+ (dense): Linear(in_features=768, out_features=768, bias=True)
42
+ (activation): Tanh()
43
+ )
44
+ )
45
+ )
46
+ (locked_dropout): LockedDropout(p=0.5)
47
+ (linear): Linear(in_features=768, out_features=21, bias=True)
48
+ (loss_function): CrossEntropyLoss()
49
+ )"
50
+ 2023-09-04 13:39:01,979 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-04 13:39:01,979 MultiCorpus: 5901 train + 1287 dev + 1505 test sentences
52
+ - NER_HIPE_2022 Corpus: 5901 train + 1287 dev + 1505 test sentences - /app/.flair/datasets/ner_hipe_2022/v2.1/hipe2020/fr/with_doc_seperator
53
+ 2023-09-04 13:39:01,979 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-04 13:39:01,979 Train: 5901 sentences
55
+ 2023-09-04 13:39:01,979 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 13:39:01,979 ----------------------------------------------------------------------------------------------------
57
+ 2023-09-04 13:39:01,979 Training Params:
58
+ 2023-09-04 13:39:01,979 - learning_rate: "5e-05"
59
+ 2023-09-04 13:39:01,979 - mini_batch_size: "8"
60
+ 2023-09-04 13:39:01,979 - max_epochs: "10"
61
+ 2023-09-04 13:39:01,979 - shuffle: "True"
62
+ 2023-09-04 13:39:01,979 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-04 13:39:01,979 Plugins:
64
+ 2023-09-04 13:39:01,979 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 13:39:01,979 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-04 13:39:01,980 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 13:39:01,980 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 13:39:01,980 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-04 13:39:01,980 Computation:
70
+ 2023-09-04 13:39:01,980 - compute on device: cuda:0
71
+ 2023-09-04 13:39:01,980 - embedding storage: none
72
+ 2023-09-04 13:39:01,980 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-04 13:39:01,980 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3"
74
+ 2023-09-04 13:39:01,980 ----------------------------------------------------------------------------------------------------
75
+ 2023-09-04 13:39:01,980 ----------------------------------------------------------------------------------------------------
76
+ 2023-09-04 13:39:14,291 epoch 1 - iter 73/738 - loss 2.44326612 - time (sec): 12.31 - samples/sec: 1238.74 - lr: 0.000005 - momentum: 0.000000
77
+ 2023-09-04 13:39:27,922 epoch 1 - iter 146/738 - loss 1.47856637 - time (sec): 25.94 - samples/sec: 1258.14 - lr: 0.000010 - momentum: 0.000000
78
+ 2023-09-04 13:39:40,565 epoch 1 - iter 219/738 - loss 1.14694216 - time (sec): 38.58 - samples/sec: 1236.59 - lr: 0.000015 - momentum: 0.000000
79
+ 2023-09-04 13:39:54,257 epoch 1 - iter 292/738 - loss 0.93905184 - time (sec): 52.28 - samples/sec: 1224.86 - lr: 0.000020 - momentum: 0.000000
80
+ 2023-09-04 13:40:08,310 epoch 1 - iter 365/738 - loss 0.80096618 - time (sec): 66.33 - samples/sec: 1223.15 - lr: 0.000025 - momentum: 0.000000
81
+ 2023-09-04 13:40:22,630 epoch 1 - iter 438/738 - loss 0.70508476 - time (sec): 80.65 - samples/sec: 1216.96 - lr: 0.000030 - momentum: 0.000000
82
+ 2023-09-04 13:40:38,180 epoch 1 - iter 511/738 - loss 0.63104936 - time (sec): 96.20 - samples/sec: 1203.40 - lr: 0.000035 - momentum: 0.000000
83
+ 2023-09-04 13:40:51,146 epoch 1 - iter 584/738 - loss 0.58093276 - time (sec): 109.16 - samples/sec: 1203.18 - lr: 0.000039 - momentum: 0.000000
84
+ 2023-09-04 13:41:07,323 epoch 1 - iter 657/738 - loss 0.53122241 - time (sec): 125.34 - samples/sec: 1190.77 - lr: 0.000044 - momentum: 0.000000
85
+ 2023-09-04 13:41:20,266 epoch 1 - iter 730/738 - loss 0.49879954 - time (sec): 138.28 - samples/sec: 1190.28 - lr: 0.000049 - momentum: 0.000000
86
+ 2023-09-04 13:41:22,063 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 13:41:22,064 EPOCH 1 done: loss 0.4968 - lr: 0.000049
88
+ 2023-09-04 13:41:36,135 DEV : loss 0.14711907505989075 - f1-score (micro avg) 0.6766
89
+ 2023-09-04 13:41:36,163 saving best model
90
+ 2023-09-04 13:41:36,647 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 13:41:50,044 epoch 2 - iter 73/738 - loss 0.13120650 - time (sec): 13.40 - samples/sec: 1225.66 - lr: 0.000049 - momentum: 0.000000
92
+ 2023-09-04 13:42:04,790 epoch 2 - iter 146/738 - loss 0.13558307 - time (sec): 28.14 - samples/sec: 1188.56 - lr: 0.000049 - momentum: 0.000000
93
+ 2023-09-04 13:42:20,969 epoch 2 - iter 219/738 - loss 0.13389094 - time (sec): 44.32 - samples/sec: 1169.25 - lr: 0.000048 - momentum: 0.000000
94
+ 2023-09-04 13:42:33,703 epoch 2 - iter 292/738 - loss 0.13115667 - time (sec): 57.05 - samples/sec: 1179.88 - lr: 0.000048 - momentum: 0.000000
95
+ 2023-09-04 13:42:46,226 epoch 2 - iter 365/738 - loss 0.13074652 - time (sec): 69.58 - samples/sec: 1192.30 - lr: 0.000047 - momentum: 0.000000
96
+ 2023-09-04 13:43:02,142 epoch 2 - iter 438/738 - loss 0.12915508 - time (sec): 85.49 - samples/sec: 1181.98 - lr: 0.000047 - momentum: 0.000000
97
+ 2023-09-04 13:43:15,964 epoch 2 - iter 511/738 - loss 0.12777004 - time (sec): 99.32 - samples/sec: 1182.98 - lr: 0.000046 - momentum: 0.000000
98
+ 2023-09-04 13:43:29,576 epoch 2 - iter 584/738 - loss 0.12429028 - time (sec): 112.93 - samples/sec: 1180.99 - lr: 0.000046 - momentum: 0.000000
99
+ 2023-09-04 13:43:42,613 epoch 2 - iter 657/738 - loss 0.12318573 - time (sec): 125.96 - samples/sec: 1183.61 - lr: 0.000045 - momentum: 0.000000
100
+ 2023-09-04 13:43:55,550 epoch 2 - iter 730/738 - loss 0.12152834 - time (sec): 138.90 - samples/sec: 1188.39 - lr: 0.000045 - momentum: 0.000000
101
+ 2023-09-04 13:43:56,718 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 13:43:56,718 EPOCH 2 done: loss 0.1220 - lr: 0.000045
103
+ 2023-09-04 13:44:14,588 DEV : loss 0.1313476413488388 - f1-score (micro avg) 0.7638
104
+ 2023-09-04 13:44:14,617 saving best model
105
+ 2023-09-04 13:44:15,947 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 13:44:28,284 epoch 3 - iter 73/738 - loss 0.05671349 - time (sec): 12.34 - samples/sec: 1230.07 - lr: 0.000044 - momentum: 0.000000
107
+ 2023-09-04 13:44:41,649 epoch 3 - iter 146/738 - loss 0.06884812 - time (sec): 25.70 - samples/sec: 1238.47 - lr: 0.000043 - momentum: 0.000000
108
+ 2023-09-04 13:44:55,404 epoch 3 - iter 219/738 - loss 0.07228850 - time (sec): 39.46 - samples/sec: 1222.20 - lr: 0.000043 - momentum: 0.000000
109
+ 2023-09-04 13:45:11,524 epoch 3 - iter 292/738 - loss 0.06995531 - time (sec): 55.58 - samples/sec: 1213.70 - lr: 0.000042 - momentum: 0.000000
110
+ 2023-09-04 13:45:26,135 epoch 3 - iter 365/738 - loss 0.07259794 - time (sec): 70.19 - samples/sec: 1194.49 - lr: 0.000042 - momentum: 0.000000
111
+ 2023-09-04 13:45:38,808 epoch 3 - iter 438/738 - loss 0.07407768 - time (sec): 82.86 - samples/sec: 1200.50 - lr: 0.000041 - momentum: 0.000000
112
+ 2023-09-04 13:45:53,814 epoch 3 - iter 511/738 - loss 0.07541008 - time (sec): 97.87 - samples/sec: 1193.07 - lr: 0.000041 - momentum: 0.000000
113
+ 2023-09-04 13:46:06,906 epoch 3 - iter 584/738 - loss 0.07337803 - time (sec): 110.96 - samples/sec: 1195.41 - lr: 0.000040 - momentum: 0.000000
114
+ 2023-09-04 13:46:20,624 epoch 3 - iter 657/738 - loss 0.07572131 - time (sec): 124.67 - samples/sec: 1194.47 - lr: 0.000040 - momentum: 0.000000
115
+ 2023-09-04 13:46:34,123 epoch 3 - iter 730/738 - loss 0.07457011 - time (sec): 138.17 - samples/sec: 1191.28 - lr: 0.000039 - momentum: 0.000000
116
+ 2023-09-04 13:46:35,620 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 13:46:35,620 EPOCH 3 done: loss 0.0744 - lr: 0.000039
118
+ 2023-09-04 13:46:53,398 DEV : loss 0.13625338673591614 - f1-score (micro avg) 0.7869
119
+ 2023-09-04 13:46:53,426 saving best model
120
+ 2023-09-04 13:46:54,791 ----------------------------------------------------------------------------------------------------
121
+ 2023-09-04 13:47:09,408 epoch 4 - iter 73/738 - loss 0.04743530 - time (sec): 14.61 - samples/sec: 1182.99 - lr: 0.000038 - momentum: 0.000000
122
+ 2023-09-04 13:47:22,832 epoch 4 - iter 146/738 - loss 0.04786156 - time (sec): 28.04 - samples/sec: 1199.93 - lr: 0.000038 - momentum: 0.000000
123
+ 2023-09-04 13:47:37,098 epoch 4 - iter 219/738 - loss 0.04448843 - time (sec): 42.31 - samples/sec: 1201.29 - lr: 0.000037 - momentum: 0.000000
124
+ 2023-09-04 13:47:50,064 epoch 4 - iter 292/738 - loss 0.04481142 - time (sec): 55.27 - samples/sec: 1205.00 - lr: 0.000037 - momentum: 0.000000
125
+ 2023-09-04 13:48:01,974 epoch 4 - iter 365/738 - loss 0.04460981 - time (sec): 67.18 - samples/sec: 1213.70 - lr: 0.000036 - momentum: 0.000000
126
+ 2023-09-04 13:48:16,980 epoch 4 - iter 438/738 - loss 0.04525369 - time (sec): 82.19 - samples/sec: 1200.51 - lr: 0.000036 - momentum: 0.000000
127
+ 2023-09-04 13:48:33,254 epoch 4 - iter 511/738 - loss 0.04819381 - time (sec): 98.46 - samples/sec: 1193.80 - lr: 0.000035 - momentum: 0.000000
128
+ 2023-09-04 13:48:45,973 epoch 4 - iter 584/738 - loss 0.04935949 - time (sec): 111.18 - samples/sec: 1193.27 - lr: 0.000035 - momentum: 0.000000
129
+ 2023-09-04 13:48:59,423 epoch 4 - iter 657/738 - loss 0.04824922 - time (sec): 124.63 - samples/sec: 1190.56 - lr: 0.000034 - momentum: 0.000000
130
+ 2023-09-04 13:49:12,677 epoch 4 - iter 730/738 - loss 0.04996471 - time (sec): 137.88 - samples/sec: 1194.19 - lr: 0.000033 - momentum: 0.000000
131
+ 2023-09-04 13:49:14,385 ----------------------------------------------------------------------------------------------------
132
+ 2023-09-04 13:49:14,386 EPOCH 4 done: loss 0.0497 - lr: 0.000033
133
+ 2023-09-04 13:49:32,177 DEV : loss 0.14975322782993317 - f1-score (micro avg) 0.8032
134
+ 2023-09-04 13:49:32,205 saving best model
135
+ 2023-09-04 13:49:33,547 ----------------------------------------------------------------------------------------------------
136
+ 2023-09-04 13:49:48,290 epoch 5 - iter 73/738 - loss 0.04353647 - time (sec): 14.74 - samples/sec: 1227.44 - lr: 0.000033 - momentum: 0.000000
137
+ 2023-09-04 13:50:01,528 epoch 5 - iter 146/738 - loss 0.03590759 - time (sec): 27.98 - samples/sec: 1215.28 - lr: 0.000032 - momentum: 0.000000
138
+ 2023-09-04 13:50:16,811 epoch 5 - iter 219/738 - loss 0.03904111 - time (sec): 43.26 - samples/sec: 1187.85 - lr: 0.000032 - momentum: 0.000000
139
+ 2023-09-04 13:50:29,758 epoch 5 - iter 292/738 - loss 0.03738277 - time (sec): 56.21 - samples/sec: 1187.90 - lr: 0.000031 - momentum: 0.000000
140
+ 2023-09-04 13:50:42,348 epoch 5 - iter 365/738 - loss 0.03701919 - time (sec): 68.80 - samples/sec: 1195.49 - lr: 0.000031 - momentum: 0.000000
141
+ 2023-09-04 13:50:55,788 epoch 5 - iter 438/738 - loss 0.03547966 - time (sec): 82.24 - samples/sec: 1188.77 - lr: 0.000030 - momentum: 0.000000
142
+ 2023-09-04 13:51:10,184 epoch 5 - iter 511/738 - loss 0.03745342 - time (sec): 96.64 - samples/sec: 1182.32 - lr: 0.000030 - momentum: 0.000000
143
+ 2023-09-04 13:51:23,925 epoch 5 - iter 584/738 - loss 0.03628508 - time (sec): 110.38 - samples/sec: 1177.95 - lr: 0.000029 - momentum: 0.000000
144
+ 2023-09-04 13:51:36,527 epoch 5 - iter 657/738 - loss 0.03647575 - time (sec): 122.98 - samples/sec: 1183.22 - lr: 0.000028 - momentum: 0.000000
145
+ 2023-09-04 13:51:52,693 epoch 5 - iter 730/738 - loss 0.03683964 - time (sec): 139.14 - samples/sec: 1183.49 - lr: 0.000028 - momentum: 0.000000
146
+ 2023-09-04 13:51:54,084 ----------------------------------------------------------------------------------------------------
147
+ 2023-09-04 13:51:54,084 EPOCH 5 done: loss 0.0370 - lr: 0.000028
148
+ 2023-09-04 13:52:11,957 DEV : loss 0.19077804684638977 - f1-score (micro avg) 0.7933
149
+ 2023-09-04 13:52:11,986 ----------------------------------------------------------------------------------------------------
150
+ 2023-09-04 13:52:27,594 epoch 6 - iter 73/738 - loss 0.02834930 - time (sec): 15.61 - samples/sec: 1157.16 - lr: 0.000027 - momentum: 0.000000
151
+ 2023-09-04 13:52:42,613 epoch 6 - iter 146/738 - loss 0.02133025 - time (sec): 30.63 - samples/sec: 1156.70 - lr: 0.000027 - momentum: 0.000000
152
+ 2023-09-04 13:52:58,195 epoch 6 - iter 219/738 - loss 0.02769820 - time (sec): 46.21 - samples/sec: 1163.21 - lr: 0.000026 - momentum: 0.000000
153
+ 2023-09-04 13:53:11,283 epoch 6 - iter 292/738 - loss 0.02666574 - time (sec): 59.30 - samples/sec: 1175.44 - lr: 0.000026 - momentum: 0.000000
154
+ 2023-09-04 13:53:24,476 epoch 6 - iter 365/738 - loss 0.02534577 - time (sec): 72.49 - samples/sec: 1181.85 - lr: 0.000025 - momentum: 0.000000
155
+ 2023-09-04 13:53:38,741 epoch 6 - iter 438/738 - loss 0.02624624 - time (sec): 86.75 - samples/sec: 1175.51 - lr: 0.000025 - momentum: 0.000000
156
+ 2023-09-04 13:53:52,542 epoch 6 - iter 511/738 - loss 0.02536390 - time (sec): 100.56 - samples/sec: 1175.88 - lr: 0.000024 - momentum: 0.000000
157
+ 2023-09-04 13:54:05,072 epoch 6 - iter 584/738 - loss 0.02502495 - time (sec): 113.09 - samples/sec: 1178.87 - lr: 0.000023 - momentum: 0.000000
158
+ 2023-09-04 13:54:17,863 epoch 6 - iter 657/738 - loss 0.02611623 - time (sec): 125.88 - samples/sec: 1181.91 - lr: 0.000023 - momentum: 0.000000
159
+ 2023-09-04 13:54:30,487 epoch 6 - iter 730/738 - loss 0.02596158 - time (sec): 138.50 - samples/sec: 1187.68 - lr: 0.000022 - momentum: 0.000000
160
+ 2023-09-04 13:54:31,774 ----------------------------------------------------------------------------------------------------
161
+ 2023-09-04 13:54:31,774 EPOCH 6 done: loss 0.0258 - lr: 0.000022
162
+ 2023-09-04 13:54:49,616 DEV : loss 0.19920460879802704 - f1-score (micro avg) 0.8066
163
+ 2023-09-04 13:54:49,656 saving best model
164
+ 2023-09-04 13:54:51,032 ----------------------------------------------------------------------------------------------------
165
+ 2023-09-04 13:55:02,883 epoch 7 - iter 73/738 - loss 0.00733074 - time (sec): 11.85 - samples/sec: 1297.71 - lr: 0.000022 - momentum: 0.000000
166
+ 2023-09-04 13:55:18,108 epoch 7 - iter 146/738 - loss 0.01089914 - time (sec): 27.07 - samples/sec: 1219.12 - lr: 0.000021 - momentum: 0.000000
167
+ 2023-09-04 13:55:29,575 epoch 7 - iter 219/738 - loss 0.01512421 - time (sec): 38.54 - samples/sec: 1240.06 - lr: 0.000021 - momentum: 0.000000
168
+ 2023-09-04 13:55:45,104 epoch 7 - iter 292/738 - loss 0.01728633 - time (sec): 54.07 - samples/sec: 1205.68 - lr: 0.000020 - momentum: 0.000000
169
+ 2023-09-04 13:55:57,489 epoch 7 - iter 365/738 - loss 0.01658937 - time (sec): 66.46 - samples/sec: 1214.60 - lr: 0.000020 - momentum: 0.000000
170
+ 2023-09-04 13:56:09,788 epoch 7 - iter 438/738 - loss 0.01645276 - time (sec): 78.75 - samples/sec: 1217.15 - lr: 0.000019 - momentum: 0.000000
171
+ 2023-09-04 13:56:27,505 epoch 7 - iter 511/738 - loss 0.01627472 - time (sec): 96.47 - samples/sec: 1198.84 - lr: 0.000018 - momentum: 0.000000
172
+ 2023-09-04 13:56:42,146 epoch 7 - iter 584/738 - loss 0.01756879 - time (sec): 111.11 - samples/sec: 1195.67 - lr: 0.000018 - momentum: 0.000000
173
+ 2023-09-04 13:56:56,291 epoch 7 - iter 657/738 - loss 0.01829967 - time (sec): 125.26 - samples/sec: 1192.94 - lr: 0.000017 - momentum: 0.000000
174
+ 2023-09-04 13:57:09,157 epoch 7 - iter 730/738 - loss 0.01839564 - time (sec): 138.12 - samples/sec: 1194.13 - lr: 0.000017 - momentum: 0.000000
175
+ 2023-09-04 13:57:10,334 ----------------------------------------------------------------------------------------------------
176
+ 2023-09-04 13:57:10,334 EPOCH 7 done: loss 0.0184 - lr: 0.000017
177
+ 2023-09-04 13:57:27,976 DEV : loss 0.20733937621116638 - f1-score (micro avg) 0.819
178
+ 2023-09-04 13:57:28,004 saving best model
179
+ 2023-09-04 13:57:29,349 ----------------------------------------------------------------------------------------------------
180
+ 2023-09-04 13:57:42,968 epoch 8 - iter 73/738 - loss 0.00755884 - time (sec): 13.62 - samples/sec: 1232.40 - lr: 0.000016 - momentum: 0.000000
181
+ 2023-09-04 13:57:57,518 epoch 8 - iter 146/738 - loss 0.01094801 - time (sec): 28.17 - samples/sec: 1215.25 - lr: 0.000016 - momentum: 0.000000
182
+ 2023-09-04 13:58:12,532 epoch 8 - iter 219/738 - loss 0.01345009 - time (sec): 43.18 - samples/sec: 1212.92 - lr: 0.000015 - momentum: 0.000000
183
+ 2023-09-04 13:58:27,267 epoch 8 - iter 292/738 - loss 0.01377918 - time (sec): 57.92 - samples/sec: 1193.53 - lr: 0.000015 - momentum: 0.000000
184
+ 2023-09-04 13:58:41,071 epoch 8 - iter 365/738 - loss 0.01339208 - time (sec): 71.72 - samples/sec: 1191.09 - lr: 0.000014 - momentum: 0.000000
185
+ 2023-09-04 13:58:55,299 epoch 8 - iter 438/738 - loss 0.01203734 - time (sec): 85.95 - samples/sec: 1191.47 - lr: 0.000013 - momentum: 0.000000
186
+ 2023-09-04 13:59:07,948 epoch 8 - iter 511/738 - loss 0.01212847 - time (sec): 98.60 - samples/sec: 1198.06 - lr: 0.000013 - momentum: 0.000000
187
+ 2023-09-04 13:59:20,292 epoch 8 - iter 584/738 - loss 0.01169538 - time (sec): 110.94 - samples/sec: 1195.53 - lr: 0.000012 - momentum: 0.000000
188
+ 2023-09-04 13:59:32,502 epoch 8 - iter 657/738 - loss 0.01207296 - time (sec): 123.15 - samples/sec: 1201.39 - lr: 0.000012 - momentum: 0.000000
189
+ 2023-09-04 13:59:46,166 epoch 8 - iter 730/738 - loss 0.01207065 - time (sec): 136.82 - samples/sec: 1201.05 - lr: 0.000011 - momentum: 0.000000
190
+ 2023-09-04 13:59:48,061 ----------------------------------------------------------------------------------------------------
191
+ 2023-09-04 13:59:48,062 EPOCH 8 done: loss 0.0121 - lr: 0.000011
192
+ 2023-09-04 14:00:05,760 DEV : loss 0.22424915432929993 - f1-score (micro avg) 0.8211
193
+ 2023-09-04 14:00:05,789 saving best model
194
+ 2023-09-04 14:00:07,330 ----------------------------------------------------------------------------------------------------
195
+ 2023-09-04 14:00:23,426 epoch 9 - iter 73/738 - loss 0.00950458 - time (sec): 16.09 - samples/sec: 1109.60 - lr: 0.000011 - momentum: 0.000000
196
+ 2023-09-04 14:00:37,770 epoch 9 - iter 146/738 - loss 0.00586520 - time (sec): 30.44 - samples/sec: 1144.33 - lr: 0.000010 - momentum: 0.000000
197
+ 2023-09-04 14:00:50,878 epoch 9 - iter 219/738 - loss 0.00633319 - time (sec): 43.55 - samples/sec: 1154.87 - lr: 0.000010 - momentum: 0.000000
198
+ 2023-09-04 14:01:04,305 epoch 9 - iter 292/738 - loss 0.00635097 - time (sec): 56.97 - samples/sec: 1170.47 - lr: 0.000009 - momentum: 0.000000
199
+ 2023-09-04 14:01:16,152 epoch 9 - iter 365/738 - loss 0.00833242 - time (sec): 68.82 - samples/sec: 1179.97 - lr: 0.000008 - momentum: 0.000000
200
+ 2023-09-04 14:01:30,812 epoch 9 - iter 438/738 - loss 0.00844827 - time (sec): 83.48 - samples/sec: 1175.24 - lr: 0.000008 - momentum: 0.000000
201
+ 2023-09-04 14:01:45,571 epoch 9 - iter 511/738 - loss 0.00906392 - time (sec): 98.24 - samples/sec: 1179.60 - lr: 0.000007 - momentum: 0.000000
202
+ 2023-09-04 14:01:58,091 epoch 9 - iter 584/738 - loss 0.00900934 - time (sec): 110.76 - samples/sec: 1192.16 - lr: 0.000007 - momentum: 0.000000
203
+ 2023-09-04 14:02:11,172 epoch 9 - iter 657/738 - loss 0.00922364 - time (sec): 123.84 - samples/sec: 1197.24 - lr: 0.000006 - momentum: 0.000000
204
+ 2023-09-04 14:02:25,334 epoch 9 - iter 730/738 - loss 0.00885846 - time (sec): 138.00 - samples/sec: 1192.42 - lr: 0.000006 - momentum: 0.000000
205
+ 2023-09-04 14:02:27,068 ----------------------------------------------------------------------------------------------------
206
+ 2023-09-04 14:02:27,069 EPOCH 9 done: loss 0.0089 - lr: 0.000006
207
+ 2023-09-04 14:02:44,747 DEV : loss 0.23065683245658875 - f1-score (micro avg) 0.8222
208
+ 2023-09-04 14:02:44,776 saving best model
209
+ 2023-09-04 14:02:46,116 ----------------------------------------------------------------------------------------------------
210
+ 2023-09-04 14:03:01,144 epoch 10 - iter 73/738 - loss 0.00620805 - time (sec): 15.03 - samples/sec: 1192.24 - lr: 0.000005 - momentum: 0.000000
211
+ 2023-09-04 14:03:13,444 epoch 10 - iter 146/738 - loss 0.00387357 - time (sec): 27.33 - samples/sec: 1204.29 - lr: 0.000004 - momentum: 0.000000
212
+ 2023-09-04 14:03:28,017 epoch 10 - iter 219/738 - loss 0.00355136 - time (sec): 41.90 - samples/sec: 1191.72 - lr: 0.000004 - momentum: 0.000000
213
+ 2023-09-04 14:03:41,576 epoch 10 - iter 292/738 - loss 0.00333989 - time (sec): 55.46 - samples/sec: 1177.08 - lr: 0.000003 - momentum: 0.000000
214
+ 2023-09-04 14:03:56,887 epoch 10 - iter 365/738 - loss 0.00509374 - time (sec): 70.77 - samples/sec: 1186.52 - lr: 0.000003 - momentum: 0.000000
215
+ 2023-09-04 14:04:10,482 epoch 10 - iter 438/738 - loss 0.00479139 - time (sec): 84.36 - samples/sec: 1186.07 - lr: 0.000002 - momentum: 0.000000
216
+ 2023-09-04 14:04:24,233 epoch 10 - iter 511/738 - loss 0.00497244 - time (sec): 98.11 - samples/sec: 1188.17 - lr: 0.000002 - momentum: 0.000000
217
+ 2023-09-04 14:04:37,794 epoch 10 - iter 584/738 - loss 0.00546920 - time (sec): 111.68 - samples/sec: 1193.67 - lr: 0.000001 - momentum: 0.000000
218
+ 2023-09-04 14:04:50,405 epoch 10 - iter 657/738 - loss 0.00520653 - time (sec): 124.29 - samples/sec: 1199.38 - lr: 0.000001 - momentum: 0.000000
219
+ 2023-09-04 14:05:05,287 epoch 10 - iter 730/738 - loss 0.00520739 - time (sec): 139.17 - samples/sec: 1186.35 - lr: 0.000000 - momentum: 0.000000
220
+ 2023-09-04 14:05:06,400 ----------------------------------------------------------------------------------------------------
221
+ 2023-09-04 14:05:06,400 EPOCH 10 done: loss 0.0052 - lr: 0.000000
222
+ 2023-09-04 14:05:24,012 DEV : loss 0.231268510222435 - f1-score (micro avg) 0.8163
223
+ 2023-09-04 14:05:24,519 ----------------------------------------------------------------------------------------------------
224
+ 2023-09-04 14:05:24,520 Loading model from best epoch ...
225
+ 2023-09-04 14:05:26,338 SequenceTagger predicts: Dictionary with 21 tags: O, S-loc, B-loc, E-loc, I-loc, S-pers, B-pers, E-pers, I-pers, S-org, B-org, E-org, I-org, S-time, B-time, E-time, I-time, S-prod, B-prod, E-prod, I-prod
226
+ 2023-09-04 14:05:41,002
227
+ Results:
228
+ - F-score (micro) 0.8057
229
+ - F-score (macro) 0.715
230
+ - Accuracy 0.6981
231
+
232
+ By class:
233
+ precision recall f1-score support
234
+
235
+ loc 0.8461 0.8904 0.8677 858
236
+ pers 0.7628 0.8026 0.7822 537
237
+ org 0.6441 0.5758 0.6080 132
238
+ time 0.5574 0.6296 0.5913 54
239
+ prod 0.7885 0.6721 0.7257 61
240
+
241
+ micro avg 0.7922 0.8197 0.8057 1642
242
+ macro avg 0.7198 0.7141 0.7150 1642
243
+ weighted avg 0.7910 0.8197 0.8045 1642
244
+
245
+ 2023-09-04 14:05:41,002 ----------------------------------------------------------------------------------------------------