Hugging Face's logo

stefan-it
/
hmbench-newseye-de-hmbyt5-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3

Token Classification
Flair
PyTorch
TensorBoard
German
sequence-tagger-model
Model card Files Metrics Community
2
stefan-it commited on
Commit
e293b19
•
1 Parent(s): a57e91f

Upload folder using huggingface_hub

Browse files
Files changed (7) hide show
  1. best-model.pt +3 -0
  2. dev.tsv +0 -0
  3. final-model.pt +3 -0
  4. loss.tsv +11 -0
  5. runs/events.out.tfevents.1697034823.6d4c7681f95b.1253.11 +3 -0
  6. test.tsv +0 -0
  7. training.log +263 -0
best-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8949796b2e1b9917d0b9837d3becbabbfc895f9f270ca0cfc447505916b7a072
3
+ size 870817519
dev.tsv ADDED
The diff for this file is too large to render. See raw diff
 
final-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:61232308cc6f801f82580197c4700d4b9d9c879783b345b447722ff68192d374
3
+ size 870817636
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 14:57:56 0.0002 0.7907 0.1321 0.3097 0.3390 0.3237 0.1939
3
+ 2 15:22:17 0.0001 0.1619 0.1600 0.2777 0.5227 0.3627 0.2228
4
+ 3 15:46:27 0.0001 0.1060 0.2169 0.2666 0.5701 0.3633 0.2231
5
+ 4 16:10:58 0.0001 0.0730 0.2578 0.2981 0.6250 0.4037 0.2546
6
+ 5 16:35:57 0.0001 0.0524 0.3427 0.2748 0.5814 0.3733 0.2315
7
+ 6 17:00:14 0.0001 0.0351 0.4194 0.2670 0.6534 0.3791 0.2357
8
+ 7 17:24:47 0.0001 0.0261 0.4073 0.2776 0.6136 0.3823 0.2379
9
+ 8 17:49:37 0.0000 0.0187 0.4144 0.3105 0.6098 0.4115 0.2605
10
+ 9 18:13:46 0.0000 0.0131 0.4809 0.2874 0.6402 0.3967 0.2491
11
+ 10 18:37:30 0.0000 0.0080 0.4764 0.2976 0.6364 0.4056 0.2555
runs/events.out.tfevents.1697034823.6d4c7681f95b.1253.11 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c6be4975f221c38fb990fabe6b3ef95f0e78cd20a1a00b64d2b62c09b65f20ac
3
+ size 2923780
test.tsv ADDED
The diff for this file is too large to render. See raw diff
 
training.log ADDED
@@ -0,0 +1,263 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-10-11 14:33:43,977 ----------------------------------------------------------------------------------------------------
2
+ 2023-10-11 14:33:43,979 Model: "SequenceTagger(
3
+ (embeddings): ByT5Embeddings(
4
+ (model): T5EncoderModel(
5
+ (shared): Embedding(384, 1472)
6
+ (encoder): T5Stack(
7
+ (embed_tokens): Embedding(384, 1472)
8
+ (block): ModuleList(
9
+ (0): T5Block(
10
+ (layer): ModuleList(
11
+ (0): T5LayerSelfAttention(
12
+ (SelfAttention): T5Attention(
13
+ (q): Linear(in_features=1472, out_features=384, bias=False)
14
+ (k): Linear(in_features=1472, out_features=384, bias=False)
15
+ (v): Linear(in_features=1472, out_features=384, bias=False)
16
+ (o): Linear(in_features=384, out_features=1472, bias=False)
17
+ (relative_attention_bias): Embedding(32, 6)
18
+ )
19
+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
20
+ (dropout): Dropout(p=0.1, inplace=False)
21
+ )
22
+ (1): T5LayerFF(
23
+ (DenseReluDense): T5DenseGatedActDense(
24
+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
25
+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
26
+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
27
+ (dropout): Dropout(p=0.1, inplace=False)
28
+ (act): NewGELUActivation()
29
+ )
30
+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
31
+ (dropout): Dropout(p=0.1, inplace=False)
32
+ )
33
+ )
34
+ )
35
+ (1-11): 11 x T5Block(
36
+ (layer): ModuleList(
37
+ (0): T5LayerSelfAttention(
38
+ (SelfAttention): T5Attention(
39
+ (q): Linear(in_features=1472, out_features=384, bias=False)
40
+ (k): Linear(in_features=1472, out_features=384, bias=False)
41
+ (v): Linear(in_features=1472, out_features=384, bias=False)
42
+ (o): Linear(in_features=384, out_features=1472, bias=False)
43
+ )
44
+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
45
+ (dropout): Dropout(p=0.1, inplace=False)
46
+ )
47
+ (1): T5LayerFF(
48
+ (DenseReluDense): T5DenseGatedActDense(
49
+ (wi_0): Linear(in_features=1472, out_features=3584, bias=False)
50
+ (wi_1): Linear(in_features=1472, out_features=3584, bias=False)
51
+ (wo): Linear(in_features=3584, out_features=1472, bias=False)
52
+ (dropout): Dropout(p=0.1, inplace=False)
53
+ (act): NewGELUActivation()
54
+ )
55
+ (layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
56
+ (dropout): Dropout(p=0.1, inplace=False)
57
+ )
58
+ )
59
+ )
60
+ )
61
+ (final_layer_norm): FusedRMSNorm(torch.Size([1472]), eps=1e-06, elementwise_affine=True)
62
+ (dropout): Dropout(p=0.1, inplace=False)
63
+ )
64
+ )
65
+ )
66
+ (locked_dropout): LockedDropout(p=0.5)
67
+ (linear): Linear(in_features=1472, out_features=17, bias=True)
68
+ (loss_function): CrossEntropyLoss()
69
+ )"
70
+ 2023-10-11 14:33:43,980 ----------------------------------------------------------------------------------------------------
71
+ 2023-10-11 14:33:43,980 MultiCorpus: 20847 train + 1123 dev + 3350 test sentences
72
+ - NER_HIPE_2022 Corpus: 20847 train + 1123 dev + 3350 test sentences - /root/.flair/datasets/ner_hipe_2022/v2.1/newseye/de/with_doc_seperator
73
+ 2023-10-11 14:33:43,980 ----------------------------------------------------------------------------------------------------
74
+ 2023-10-11 14:33:43,980 Train: 20847 sentences
75
+ 2023-10-11 14:33:43,980 (train_with_dev=False, train_with_test=False)
76
+ 2023-10-11 14:33:43,980 ----------------------------------------------------------------------------------------------------
77
+ 2023-10-11 14:33:43,980 Training Params:
78
+ 2023-10-11 14:33:43,980 - learning_rate: "0.00016"
79
+ 2023-10-11 14:33:43,980 - mini_batch_size: "4"
80
+ 2023-10-11 14:33:43,981 - max_epochs: "10"
81
+ 2023-10-11 14:33:43,981 - shuffle: "True"
82
+ 2023-10-11 14:33:43,981 ----------------------------------------------------------------------------------------------------
83
+ 2023-10-11 14:33:43,981 Plugins:
84
+ 2023-10-11 14:33:43,981 - TensorboardLogger
85
+ 2023-10-11 14:33:43,981 - LinearScheduler | warmup_fraction: '0.1'
86
+ 2023-10-11 14:33:43,981 ----------------------------------------------------------------------------------------------------
87
+ 2023-10-11 14:33:43,981 Final evaluation on model from best epoch (best-model.pt)
88
+ 2023-10-11 14:33:43,981 - metric: "('micro avg', 'f1-score')"
89
+ 2023-10-11 14:33:43,981 ----------------------------------------------------------------------------------------------------
90
+ 2023-10-11 14:33:43,981 Computation:
91
+ 2023-10-11 14:33:43,981 - compute on device: cuda:0
92
+ 2023-10-11 14:33:43,981 - embedding storage: none
93
+ 2023-10-11 14:33:43,982 ----------------------------------------------------------------------------------------------------
94
+ 2023-10-11 14:33:43,982 Model training base path: "hmbench-newseye/de-hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax-bs4-wsFalse-e10-lr0.00016-poolingfirst-layers-1-crfFalse-3"
95
+ 2023-10-11 14:33:43,982 ----------------------------------------------------------------------------------------------------
96
+ 2023-10-11 14:33:43,982 ----------------------------------------------------------------------------------------------------
97
+ 2023-10-11 14:33:43,982 Logging anything other than scalars to TensorBoard is currently not supported.
98
+ 2023-10-11 14:36:06,547 epoch 1 - iter 521/5212 - loss 2.76662736 - time (sec): 142.56 - samples/sec: 262.14 - lr: 0.000016 - momentum: 0.000000
99
+ 2023-10-11 14:38:27,452 epoch 1 - iter 1042/5212 - loss 2.28624082 - time (sec): 283.47 - samples/sec: 270.79 - lr: 0.000032 - momentum: 0.000000
100
+ 2023-10-11 14:40:44,639 epoch 1 - iter 1563/5212 - loss 1.79645371 - time (sec): 420.66 - samples/sec: 269.24 - lr: 0.000048 - momentum: 0.000000
101
+ 2023-10-11 14:43:01,808 epoch 1 - iter 2084/5212 - loss 1.46859051 - time (sec): 557.82 - samples/sec: 267.94 - lr: 0.000064 - momentum: 0.000000
102
+ 2023-10-11 14:45:26,500 epoch 1 - iter 2605/5212 - loss 1.27159482 - time (sec): 702.52 - samples/sec: 266.51 - lr: 0.000080 - momentum: 0.000000
103
+ 2023-10-11 14:47:48,763 epoch 1 - iter 3126/5212 - loss 1.12283149 - time (sec): 844.78 - samples/sec: 264.51 - lr: 0.000096 - momentum: 0.000000
104
+ 2023-10-11 14:50:12,100 epoch 1 - iter 3647/5212 - loss 1.01175361 - time (sec): 988.12 - samples/sec: 261.77 - lr: 0.000112 - momentum: 0.000000
105
+ 2023-10-11 14:52:34,157 epoch 1 - iter 4168/5212 - loss 0.93063939 - time (sec): 1130.17 - samples/sec: 259.51 - lr: 0.000128 - momentum: 0.000000
106
+ 2023-10-11 14:54:53,773 epoch 1 - iter 4689/5212 - loss 0.85556421 - time (sec): 1269.79 - samples/sec: 260.71 - lr: 0.000144 - momentum: 0.000000
107
+ 2023-10-11 14:57:19,976 epoch 1 - iter 5210/5212 - loss 0.79086304 - time (sec): 1415.99 - samples/sec: 259.36 - lr: 0.000160 - momentum: 0.000000
108
+ 2023-10-11 14:57:20,519 ----------------------------------------------------------------------------------------------------
109
+ 2023-10-11 14:57:20,519 EPOCH 1 done: loss 0.7907 - lr: 0.000160
110
+ 2023-10-11 14:57:56,736 DEV : loss 0.13207665085792542 - f1-score (micro avg) 0.3237
111
+ 2023-10-11 14:57:56,789 saving best model
112
+ 2023-10-11 14:57:57,698 ----------------------------------------------------------------------------------------------------
113
+ 2023-10-11 15:00:22,791 epoch 2 - iter 521/5212 - loss 0.19332337 - time (sec): 145.09 - samples/sec: 254.59 - lr: 0.000158 - momentum: 0.000000
114
+ 2023-10-11 15:02:45,528 epoch 2 - iter 1042/5212 - loss 0.18215094 - time (sec): 287.83 - samples/sec: 254.97 - lr: 0.000156 - momentum: 0.000000
115
+ 2023-10-11 15:05:10,335 epoch 2 - iter 1563/5212 - loss 0.18444577 - time (sec): 432.63 - samples/sec: 260.62 - lr: 0.000155 - momentum: 0.000000
116
+ 2023-10-11 15:07:31,990 epoch 2 - iter 2084/5212 - loss 0.18224841 - time (sec): 574.29 - samples/sec: 260.31 - lr: 0.000153 - momentum: 0.000000
117
+ 2023-10-11 15:09:52,786 epoch 2 - iter 2605/5212 - loss 0.17760403 - time (sec): 715.09 - samples/sec: 258.27 - lr: 0.000151 - momentum: 0.000000
118
+ 2023-10-11 15:12:16,111 epoch 2 - iter 3126/5212 - loss 0.17321912 - time (sec): 858.41 - samples/sec: 258.53 - lr: 0.000149 - momentum: 0.000000
119
+ 2023-10-11 15:14:34,077 epoch 2 - iter 3647/5212 - loss 0.17155961 - time (sec): 996.38 - samples/sec: 256.43 - lr: 0.000148 - momentum: 0.000000
120
+ 2023-10-11 15:16:56,628 epoch 2 - iter 4168/5212 - loss 0.16731547 - time (sec): 1138.93 - samples/sec: 256.39 - lr: 0.000146 - momentum: 0.000000
121
+ 2023-10-11 15:19:19,074 epoch 2 - iter 4689/5212 - loss 0.16554814 - time (sec): 1281.37 - samples/sec: 257.92 - lr: 0.000144 - momentum: 0.000000
122
+ 2023-10-11 15:21:37,400 epoch 2 - iter 5210/5212 - loss 0.16192292 - time (sec): 1419.70 - samples/sec: 258.75 - lr: 0.000142 - momentum: 0.000000
123
+ 2023-10-11 15:21:37,838 ----------------------------------------------------------------------------------------------------
124
+ 2023-10-11 15:21:37,838 EPOCH 2 done: loss 0.1619 - lr: 0.000142
125
+ 2023-10-11 15:22:17,665 DEV : loss 0.16001686453819275 - f1-score (micro avg) 0.3627
126
+ 2023-10-11 15:22:17,722 saving best model
127
+ 2023-10-11 15:22:20,336 ----------------------------------------------------------------------------------------------------
128
+ 2023-10-11 15:24:36,175 epoch 3 - iter 521/5212 - loss 0.09560052 - time (sec): 135.83 - samples/sec: 257.42 - lr: 0.000140 - momentum: 0.000000
129
+ 2023-10-11 15:26:53,189 epoch 3 - iter 1042/5212 - loss 0.10144802 - time (sec): 272.85 - samples/sec: 260.58 - lr: 0.000139 - momentum: 0.000000
130
+ 2023-10-11 15:29:13,216 epoch 3 - iter 1563/5212 - loss 0.09999435 - time (sec): 412.88 - samples/sec: 258.87 - lr: 0.000137 - momentum: 0.000000
131
+ 2023-10-11 15:31:36,953 epoch 3 - iter 2084/5212 - loss 0.10591845 - time (sec): 556.61 - samples/sec: 261.87 - lr: 0.000135 - momentum: 0.000000
132
+ 2023-10-11 15:33:57,505 epoch 3 - iter 2605/5212 - loss 0.11024910 - time (sec): 697.16 - samples/sec: 263.88 - lr: 0.000133 - momentum: 0.000000
133
+ 2023-10-11 15:36:17,912 epoch 3 - iter 3126/5212 - loss 0.10967581 - time (sec): 837.57 - samples/sec: 262.61 - lr: 0.000132 - momentum: 0.000000
134
+ 2023-10-11 15:38:38,424 epoch 3 - iter 3647/5212 - loss 0.10727296 - time (sec): 978.08 - samples/sec: 261.21 - lr: 0.000130 - momentum: 0.000000
135
+ 2023-10-11 15:40:59,896 epoch 3 - iter 4168/5212 - loss 0.10769948 - time (sec): 1119.55 - samples/sec: 261.35 - lr: 0.000128 - momentum: 0.000000
136
+ 2023-10-11 15:43:20,757 epoch 3 - iter 4689/5212 - loss 0.10703189 - time (sec): 1260.42 - samples/sec: 260.75 - lr: 0.000126 - momentum: 0.000000
137
+ 2023-10-11 15:45:46,414 epoch 3 - iter 5210/5212 - loss 0.10585857 - time (sec): 1406.07 - samples/sec: 261.24 - lr: 0.000124 - momentum: 0.000000
138
+ 2023-10-11 15:45:46,886 ----------------------------------------------------------------------------------------------------
139
+ 2023-10-11 15:45:46,886 EPOCH 3 done: loss 0.1060 - lr: 0.000124
140
+ 2023-10-11 15:46:27,270 DEV : loss 0.21690955758094788 - f1-score (micro avg) 0.3633
141
+ 2023-10-11 15:46:27,322 saving best model
142
+ 2023-10-11 15:46:29,931 ----------------------------------------------------------------------------------------------------
143
+ 2023-10-11 15:48:54,172 epoch 4 - iter 521/5212 - loss 0.07319839 - time (sec): 144.24 - samples/sec: 244.92 - lr: 0.000123 - momentum: 0.000000
144
+ 2023-10-11 15:51:17,181 epoch 4 - iter 1042/5212 - loss 0.07369046 - time (sec): 287.25 - samples/sec: 250.39 - lr: 0.000121 - momentum: 0.000000
145
+ 2023-10-11 15:53:41,453 epoch 4 - iter 1563/5212 - loss 0.07241720 - time (sec): 431.52 - samples/sec: 253.60 - lr: 0.000119 - momentum: 0.000000
146
+ 2023-10-11 15:56:04,766 epoch 4 - iter 2084/5212 - loss 0.07187332 - time (sec): 574.83 - samples/sec: 251.80 - lr: 0.000117 - momentum: 0.000000
147
+ 2023-10-11 15:58:30,038 epoch 4 - iter 2605/5212 - loss 0.07110536 - time (sec): 720.10 - samples/sec: 256.81 - lr: 0.000116 - momentum: 0.000000
148
+ 2023-10-11 16:00:49,154 epoch 4 - iter 3126/5212 - loss 0.07215527 - time (sec): 859.22 - samples/sec: 256.21 - lr: 0.000114 - momentum: 0.000000
149
+ 2023-10-11 16:03:11,988 epoch 4 - iter 3647/5212 - loss 0.07322367 - time (sec): 1002.05 - samples/sec: 257.20 - lr: 0.000112 - momentum: 0.000000
150
+ 2023-10-11 16:05:38,070 epoch 4 - iter 4168/5212 - loss 0.07331747 - time (sec): 1148.14 - samples/sec: 259.47 - lr: 0.000110 - momentum: 0.000000
151
+ 2023-10-11 16:07:57,178 epoch 4 - iter 4689/5212 - loss 0.07365219 - time (sec): 1287.24 - samples/sec: 257.98 - lr: 0.000108 - momentum: 0.000000
152
+ 2023-10-11 16:10:18,604 epoch 4 - iter 5210/5212 - loss 0.07301371 - time (sec): 1428.67 - samples/sec: 257.16 - lr: 0.000107 - momentum: 0.000000
153
+ 2023-10-11 16:10:19,011 ----------------------------------------------------------------------------------------------------
154
+ 2023-10-11 16:10:19,011 EPOCH 4 done: loss 0.0730 - lr: 0.000107
155
+ 2023-10-11 16:10:58,573 DEV : loss 0.2578122615814209 - f1-score (micro avg) 0.4037
156
+ 2023-10-11 16:10:58,626 saving best model
157
+ 2023-10-11 16:11:01,254 ----------------------------------------------------------------------------------------------------
158
+ 2023-10-11 16:13:24,831 epoch 5 - iter 521/5212 - loss 0.04596828 - time (sec): 143.57 - samples/sec: 250.03 - lr: 0.000105 - momentum: 0.000000
159
+ 2023-10-11 16:15:52,154 epoch 5 - iter 1042/5212 - loss 0.05176257 - time (sec): 290.89 - samples/sec: 253.07 - lr: 0.000103 - momentum: 0.000000
160
+ 2023-10-11 16:18:26,212 epoch 5 - iter 1563/5212 - loss 0.05301496 - time (sec): 444.95 - samples/sec: 245.48 - lr: 0.000101 - momentum: 0.000000
161
+ 2023-10-11 16:20:56,560 epoch 5 - iter 2084/5212 - loss 0.05310164 - time (sec): 595.30 - samples/sec: 244.83 - lr: 0.000100 - momentum: 0.000000
162
+ 2023-10-11 16:23:29,574 epoch 5 - iter 2605/5212 - loss 0.05219774 - time (sec): 748.32 - samples/sec: 246.05 - lr: 0.000098 - momentum: 0.000000
163
+ 2023-10-11 16:25:52,444 epoch 5 - iter 3126/5212 - loss 0.05195458 - time (sec): 891.19 - samples/sec: 246.25 - lr: 0.000096 - momentum: 0.000000
164
+ 2023-10-11 16:28:14,834 epoch 5 - iter 3647/5212 - loss 0.05186622 - time (sec): 1033.58 - samples/sec: 247.88 - lr: 0.000094 - momentum: 0.000000
165
+ 2023-10-11 16:30:31,671 epoch 5 - iter 4168/5212 - loss 0.05189342 - time (sec): 1170.41 - samples/sec: 248.98 - lr: 0.000092 - momentum: 0.000000
166
+ 2023-10-11 16:32:51,894 epoch 5 - iter 4689/5212 - loss 0.05074979 - time (sec): 1310.64 - samples/sec: 251.02 - lr: 0.000091 - momentum: 0.000000
167
+ 2023-10-11 16:35:18,427 epoch 5 - iter 5210/5212 - loss 0.05240335 - time (sec): 1457.17 - samples/sec: 252.09 - lr: 0.000089 - momentum: 0.000000
168
+ 2023-10-11 16:35:18,890 ----------------------------------------------------------------------------------------------------
169
+ 2023-10-11 16:35:18,891 EPOCH 5 done: loss 0.0524 - lr: 0.000089
170
+ 2023-10-11 16:35:57,847 DEV : loss 0.3427276015281677 - f1-score (micro avg) 0.3733
171
+ 2023-10-11 16:35:57,906 ----------------------------------------------------------------------------------------------------
172
+ 2023-10-11 16:38:16,413 epoch 6 - iter 521/5212 - loss 0.03453889 - time (sec): 138.50 - samples/sec: 243.67 - lr: 0.000087 - momentum: 0.000000
173
+ 2023-10-11 16:40:36,448 epoch 6 - iter 1042/5212 - loss 0.03594338 - time (sec): 278.54 - samples/sec: 245.57 - lr: 0.000085 - momentum: 0.000000
174
+ 2023-10-11 16:42:56,379 epoch 6 - iter 1563/5212 - loss 0.03623246 - time (sec): 418.47 - samples/sec: 250.12 - lr: 0.000084 - momentum: 0.000000
175
+ 2023-10-11 16:45:13,717 epoch 6 - iter 2084/5212 - loss 0.03614444 - time (sec): 555.81 - samples/sec: 253.09 - lr: 0.000082 - momentum: 0.000000
176
+ 2023-10-11 16:47:33,364 epoch 6 - iter 2605/5212 - loss 0.03645808 - time (sec): 695.46 - samples/sec: 255.18 - lr: 0.000080 - momentum: 0.000000
177
+ 2023-10-11 16:49:50,937 epoch 6 - iter 3126/5212 - loss 0.03572045 - time (sec): 833.03 - samples/sec: 255.72 - lr: 0.000078 - momentum: 0.000000
178
+ 2023-10-11 16:52:13,879 epoch 6 - iter 3647/5212 - loss 0.03557647 - time (sec): 975.97 - samples/sec: 258.76 - lr: 0.000076 - momentum: 0.000000
179
+ 2023-10-11 16:54:39,082 epoch 6 - iter 4168/5212 - loss 0.03568794 - time (sec): 1121.17 - samples/sec: 258.91 - lr: 0.000075 - momentum: 0.000000
180
+ 2023-10-11 16:57:09,848 epoch 6 - iter 4689/5212 - loss 0.03525994 - time (sec): 1271.94 - samples/sec: 259.16 - lr: 0.000073 - momentum: 0.000000
181
+ 2023-10-11 16:59:33,202 epoch 6 - iter 5210/5212 - loss 0.03514270 - time (sec): 1415.29 - samples/sec: 259.42 - lr: 0.000071 - momentum: 0.000000
182
+ 2023-10-11 16:59:33,838 ----------------------------------------------------------------------------------------------------
183
+ 2023-10-11 16:59:33,838 EPOCH 6 done: loss 0.0351 - lr: 0.000071
184
+ 2023-10-11 17:00:14,726 DEV : loss 0.4193594753742218 - f1-score (micro avg) 0.3791
185
+ 2023-10-11 17:00:14,781 ----------------------------------------------------------------------------------------------------
186
+ 2023-10-11 17:02:44,618 epoch 7 - iter 521/5212 - loss 0.02269233 - time (sec): 149.83 - samples/sec: 267.66 - lr: 0.000069 - momentum: 0.000000
187
+ 2023-10-11 17:05:04,738 epoch 7 - iter 1042/5212 - loss 0.02635316 - time (sec): 289.95 - samples/sec: 262.02 - lr: 0.000068 - momentum: 0.000000
188
+ 2023-10-11 17:07:27,032 epoch 7 - iter 1563/5212 - loss 0.02308396 - time (sec): 432.25 - samples/sec: 262.49 - lr: 0.000066 - momentum: 0.000000
189
+ 2023-10-11 17:09:48,465 epoch 7 - iter 2084/5212 - loss 0.02400360 - time (sec): 573.68 - samples/sec: 264.91 - lr: 0.000064 - momentum: 0.000000
190
+ 2023-10-11 17:12:06,814 epoch 7 - iter 2605/5212 - loss 0.02542856 - time (sec): 712.03 - samples/sec: 260.99 - lr: 0.000062 - momentum: 0.000000
191
+ 2023-10-11 17:14:31,094 epoch 7 - iter 3126/5212 - loss 0.02631394 - time (sec): 856.31 - samples/sec: 260.96 - lr: 0.000060 - momentum: 0.000000
192
+ 2023-10-11 17:16:53,608 epoch 7 - iter 3647/5212 - loss 0.02640215 - time (sec): 998.82 - samples/sec: 259.79 - lr: 0.000059 - momentum: 0.000000
193
+ 2023-10-11 17:19:14,821 epoch 7 - iter 4168/5212 - loss 0.02608981 - time (sec): 1140.04 - samples/sec: 258.59 - lr: 0.000057 - momentum: 0.000000
194
+ 2023-10-11 17:21:40,650 epoch 7 - iter 4689/5212 - loss 0.02628534 - time (sec): 1285.87 - samples/sec: 257.56 - lr: 0.000055 - momentum: 0.000000
195
+ 2023-10-11 17:24:06,529 epoch 7 - iter 5210/5212 - loss 0.02607886 - time (sec): 1431.75 - samples/sec: 256.60 - lr: 0.000053 - momentum: 0.000000
196
+ 2023-10-11 17:24:06,965 ----------------------------------------------------------------------------------------------------
197
+ 2023-10-11 17:24:06,965 EPOCH 7 done: loss 0.0261 - lr: 0.000053
198
+ 2023-10-11 17:24:47,377 DEV : loss 0.4072570204734802 - f1-score (micro avg) 0.3823
199
+ 2023-10-11 17:24:47,431 ----------------------------------------------------------------------------------------------------
200
+ 2023-10-11 17:27:15,714 epoch 8 - iter 521/5212 - loss 0.01684084 - time (sec): 148.28 - samples/sec: 248.06 - lr: 0.000052 - momentum: 0.000000
201
+ 2023-10-11 17:29:41,615 epoch 8 - iter 1042/5212 - loss 0.01921364 - time (sec): 294.18 - samples/sec: 252.08 - lr: 0.000050 - momentum: 0.000000
202
+ 2023-10-11 17:32:06,810 epoch 8 - iter 1563/5212 - loss 0.02074029 - time (sec): 439.38 - samples/sec: 250.98 - lr: 0.000048 - momentum: 0.000000
203
+ 2023-10-11 17:34:30,337 epoch 8 - iter 2084/5212 - loss 0.01866275 - time (sec): 582.90 - samples/sec: 252.36 - lr: 0.000046 - momentum: 0.000000
204
+ 2023-10-11 17:36:55,146 epoch 8 - iter 2605/5212 - loss 0.01912678 - time (sec): 727.71 - samples/sec: 253.80 - lr: 0.000044 - momentum: 0.000000
205
+ 2023-10-11 17:39:18,799 epoch 8 - iter 3126/5212 - loss 0.01960685 - time (sec): 871.37 - samples/sec: 253.85 - lr: 0.000043 - momentum: 0.000000
206
+ 2023-10-11 17:41:42,158 epoch 8 - iter 3647/5212 - loss 0.01902745 - time (sec): 1014.72 - samples/sec: 253.30 - lr: 0.000041 - momentum: 0.000000
207
+ 2023-10-11 17:44:07,681 epoch 8 - iter 4168/5212 - loss 0.01851736 - time (sec): 1160.25 - samples/sec: 253.42 - lr: 0.000039 - momentum: 0.000000
208
+ 2023-10-11 17:46:34,426 epoch 8 - iter 4689/5212 - loss 0.01823475 - time (sec): 1306.99 - samples/sec: 253.91 - lr: 0.000037 - momentum: 0.000000
209
+ 2023-10-11 17:48:56,985 epoch 8 - iter 5210/5212 - loss 0.01875215 - time (sec): 1449.55 - samples/sec: 253.25 - lr: 0.000036 - momentum: 0.000000
210
+ 2023-10-11 17:48:57,706 ----------------------------------------------------------------------------------------------------
211
+ 2023-10-11 17:48:57,707 EPOCH 8 done: loss 0.0187 - lr: 0.000036
212
+ 2023-10-11 17:49:37,133 DEV : loss 0.41436994075775146 - f1-score (micro avg) 0.4115
213
+ 2023-10-11 17:49:37,186 saving best model
214
+ 2023-10-11 17:49:39,805 ----------------------------------------------------------------------------------------------------
215
+ 2023-10-11 17:52:05,310 epoch 9 - iter 521/5212 - loss 0.01196532 - time (sec): 145.50 - samples/sec: 263.89 - lr: 0.000034 - momentum: 0.000000
216
+ 2023-10-11 17:54:27,376 epoch 9 - iter 1042/5212 - loss 0.01333594 - time (sec): 287.57 - samples/sec: 264.07 - lr: 0.000032 - momentum: 0.000000
217
+ 2023-10-11 17:56:48,142 epoch 9 - iter 1563/5212 - loss 0.01211283 - time (sec): 428.33 - samples/sec: 260.34 - lr: 0.000030 - momentum: 0.000000
218
+ 2023-10-11 17:59:07,321 epoch 9 - iter 2084/5212 - loss 0.01222749 - time (sec): 567.51 - samples/sec: 257.43 - lr: 0.000028 - momentum: 0.000000
219
+ 2023-10-11 18:01:27,565 epoch 9 - iter 2605/5212 - loss 0.01192194 - time (sec): 707.76 - samples/sec: 259.45 - lr: 0.000027 - momentum: 0.000000
220
+ 2023-10-11 18:03:45,920 epoch 9 - iter 3126/5212 - loss 0.01186385 - time (sec): 846.11 - samples/sec: 259.03 - lr: 0.000025 - momentum: 0.000000
221
+ 2023-10-11 18:06:04,727 epoch 9 - iter 3647/5212 - loss 0.01241909 - time (sec): 984.92 - samples/sec: 260.05 - lr: 0.000023 - momentum: 0.000000
222
+ 2023-10-11 18:08:24,518 epoch 9 - iter 4168/5212 - loss 0.01204146 - time (sec): 1124.71 - samples/sec: 261.02 - lr: 0.000021 - momentum: 0.000000
223
+ 2023-10-11 18:10:47,837 epoch 9 - iter 4689/5212 - loss 0.01273801 - time (sec): 1268.03 - samples/sec: 260.93 - lr: 0.000020 - momentum: 0.000000
224
+ 2023-10-11 18:13:07,309 epoch 9 - iter 5210/5212 - loss 0.01307196 - time (sec): 1407.50 - samples/sec: 260.86 - lr: 0.000018 - momentum: 0.000000
225
+ 2023-10-11 18:13:07,901 ----------------------------------------------------------------------------------------------------
226
+ 2023-10-11 18:13:07,901 EPOCH 9 done: loss 0.0131 - lr: 0.000018
227
+ 2023-10-11 18:13:46,547 DEV : loss 0.4808931350708008 - f1-score (micro avg) 0.3967
228
+ 2023-10-11 18:13:46,607 ----------------------------------------------------------------------------------------------------
229
+ 2023-10-11 18:16:05,453 epoch 10 - iter 521/5212 - loss 0.00668555 - time (sec): 138.84 - samples/sec: 262.74 - lr: 0.000016 - momentum: 0.000000
230
+ 2023-10-11 18:18:22,229 epoch 10 - iter 1042/5212 - loss 0.00706135 - time (sec): 275.62 - samples/sec: 260.81 - lr: 0.000014 - momentum: 0.000000
231
+ 2023-10-11 18:20:40,291 epoch 10 - iter 1563/5212 - loss 0.00669620 - time (sec): 413.68 - samples/sec: 262.77 - lr: 0.000012 - momentum: 0.000000
232
+ 2023-10-11 18:22:57,132 epoch 10 - iter 2084/5212 - loss 0.00716732 - time (sec): 550.52 - samples/sec: 261.09 - lr: 0.000011 - momentum: 0.000000
233
+ 2023-10-11 18:25:18,034 epoch 10 - iter 2605/5212 - loss 0.00737559 - time (sec): 691.43 - samples/sec: 264.83 - lr: 0.000009 - momentum: 0.000000
234
+ 2023-10-11 18:27:35,108 epoch 10 - iter 3126/5212 - loss 0.00746666 - time (sec): 828.50 - samples/sec: 264.40 - lr: 0.000007 - momentum: 0.000000
235
+ 2023-10-11 18:29:52,239 epoch 10 - iter 3647/5212 - loss 0.00801610 - time (sec): 965.63 - samples/sec: 264.55 - lr: 0.000005 - momentum: 0.000000
236
+ 2023-10-11 18:32:08,536 epoch 10 - iter 4168/5212 - loss 0.00797516 - time (sec): 1101.93 - samples/sec: 263.54 - lr: 0.000004 - momentum: 0.000000
237
+ 2023-10-11 18:34:30,744 epoch 10 - iter 4689/5212 - loss 0.00813383 - time (sec): 1244.13 - samples/sec: 265.19 - lr: 0.000002 - momentum: 0.000000
238
+ 2023-10-11 18:36:51,864 epoch 10 - iter 5210/5212 - loss 0.00803504 - time (sec): 1385.26 - samples/sec: 265.22 - lr: 0.000000 - momentum: 0.000000
239
+ 2023-10-11 18:36:52,264 ----------------------------------------------------------------------------------------------------
240
+ 2023-10-11 18:36:52,265 EPOCH 10 done: loss 0.0080 - lr: 0.000000
241
+ 2023-10-11 18:37:30,395 DEV : loss 0.47644102573394775 - f1-score (micro avg) 0.4056
242
+ 2023-10-11 18:37:31,324 ----------------------------------------------------------------------------------------------------
243
+ 2023-10-11 18:37:31,326 Loading model from best epoch ...
244
+ 2023-10-11 18:37:35,775 SequenceTagger predicts: Dictionary with 17 tags: O, S-LOC, B-LOC, E-LOC, I-LOC, S-PER, B-PER, E-PER, I-PER, S-ORG, B-ORG, E-ORG, I-ORG, S-HumanProd, B-HumanProd, E-HumanProd, I-HumanProd
245
+ 2023-10-11 18:39:14,485
246
+ Results:
247
+ - F-score (micro) 0.4756
248
+ - F-score (macro) 0.3235
249
+ - Accuracy 0.317
250
+
251
+ By class:
252
+ precision recall f1-score support
253
+
254
+ LOC 0.4876 0.5972 0.5368 1214
255
+ PER 0.4418 0.4790 0.4596 808
256
+ ORG 0.2923 0.3031 0.2976 353
257
+ HumanProd 0.0000 0.0000 0.0000 15
258
+
259
+ micro avg 0.4455 0.5100 0.4756 2390
260
+ macro avg 0.3054 0.3448 0.3235 2390
261
+ weighted avg 0.4402 0.5100 0.4720 2390
262
+
263
+ 2023-10-11 18:39:14,485 ----------------------------------------------------------------------------------------------------