Hugging Face's logo

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

Dataset card Files Community
stefan-it commited on
Commit
c2ea5e2
•
1 Parent(s): 344e033

Upload folder using huggingface_hub

Browse files
Files changed (9) hide show
  1. __pycache__/flair-fine-tuner.cpython-39.pyc +0 -0
  2. __pycache__/utils.cpython-39.pyc +0 -0
  3. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/best-model.pt +3 -0
  4. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/dev.tsv +0 -0
  5. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/final-model.pt +3 -0
  6. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/loss.tsv +11 -0
  7. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/test.tsv +0 -0
  8. hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/training.log +245 -0
  9. training_params.json +8 -0
__pycache__/flair-fine-tuner.cpython-39.pyc ADDED
Binary file (3.26 kB). View file
 
__pycache__/utils.cpython-39.pyc ADDED
Binary file (4.6 kB). View file
 
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/best-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:350fcbfe586694a22d63197b8fee2bb1dc7dec651585844f9aaa00ce90c07e01
3
+ size 443334288
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/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-lr3e-05-poolingfirst-layers-1-crfFalse-1/final-model.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c87a87bea36cb13135fa3b2cec4cdd677e488912562ef52061dd74342b25d186
3
+ size 443334491
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/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 09:24:17 0.0000 0.6392 0.1547 0.6683 0.6901 0.6791 0.5435
3
+ 2 09:26:55 0.0000 0.1268 0.0999 0.7742 0.8288 0.8006 0.6881
4
+ 3 09:29:32 0.0000 0.0718 0.1222 0.7759 0.7990 0.7872 0.6835
5
+ 4 09:32:08 0.0000 0.0467 0.1691 0.7696 0.8190 0.7936 0.6938
6
+ 5 09:34:46 0.0000 0.0343 0.1684 0.7871 0.8299 0.8079 0.7017
7
+ 6 09:37:25 0.0000 0.0238 0.1733 0.8149 0.8345 0.8246 0.7260
8
+ 7 09:40:05 0.0000 0.0185 0.1819 0.8184 0.8310 0.8247 0.7339
9
+ 8 09:42:45 0.0000 0.0128 0.1916 0.8100 0.8425 0.8259 0.7333
10
+ 9 09:45:24 0.0000 0.0105 0.1950 0.8136 0.8402 0.8267 0.7299
11
+ 10 09:48:03 0.0000 0.0074 0.1974 0.8137 0.8408 0.8270 0.7329
hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1/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-lr3e-05-poolingfirst-layers-1-crfFalse-1/training.log ADDED
@@ -0,0 +1,245 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2023-09-04 09:21:49,223 ----------------------------------------------------------------------------------------------------
2
+ 2023-09-04 09:21:49,224 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 09:21:49,224 ----------------------------------------------------------------------------------------------------
51
+ 2023-09-04 09:21:49,224 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 09:21:49,224 ----------------------------------------------------------------------------------------------------
54
+ 2023-09-04 09:21:49,224 Train: 5901 sentences
55
+ 2023-09-04 09:21:49,224 (train_with_dev=False, train_with_test=False)
56
+ 2023-09-04 09:21:49,224 ----------------------------------------------------------------------------------------------------
57
+ 2023-09-04 09:21:49,224 Training Params:
58
+ 2023-09-04 09:21:49,224 - learning_rate: "3e-05"
59
+ 2023-09-04 09:21:49,224 - mini_batch_size: "8"
60
+ 2023-09-04 09:21:49,224 - max_epochs: "10"
61
+ 2023-09-04 09:21:49,225 - shuffle: "True"
62
+ 2023-09-04 09:21:49,225 ----------------------------------------------------------------------------------------------------
63
+ 2023-09-04 09:21:49,225 Plugins:
64
+ 2023-09-04 09:21:49,225 - LinearScheduler | warmup_fraction: '0.1'
65
+ 2023-09-04 09:21:49,225 ----------------------------------------------------------------------------------------------------
66
+ 2023-09-04 09:21:49,225 Final evaluation on model from best epoch (best-model.pt)
67
+ 2023-09-04 09:21:49,225 - metric: "('micro avg', 'f1-score')"
68
+ 2023-09-04 09:21:49,225 ----------------------------------------------------------------------------------------------------
69
+ 2023-09-04 09:21:49,225 Computation:
70
+ 2023-09-04 09:21:49,225 - compute on device: cuda:0
71
+ 2023-09-04 09:21:49,225 - embedding storage: none
72
+ 2023-09-04 09:21:49,225 ----------------------------------------------------------------------------------------------------
73
+ 2023-09-04 09:21:49,225 Model training base path: "hmbench-hipe2020/fr-dbmdz/bert-base-historic-multilingual-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1"
74
+ 2023-09-04 09:21:49,225 ----------------------------------------------------------------------------------------------------
75
+ 2023-09-04 09:21:49,225 ----------------------------------------------------------------------------------------------------
76
+ 2023-09-04 09:22:02,605 epoch 1 - iter 73/738 - loss 2.99077951 - time (sec): 13.38 - samples/sec: 1246.48 - lr: 0.000003 - momentum: 0.000000
77
+ 2023-09-04 09:22:15,462 epoch 1 - iter 146/738 - loss 1.99727633 - time (sec): 26.24 - samples/sec: 1255.96 - lr: 0.000006 - momentum: 0.000000
78
+ 2023-09-04 09:22:30,938 epoch 1 - iter 219/738 - loss 1.45706905 - time (sec): 41.71 - samples/sec: 1243.17 - lr: 0.000009 - momentum: 0.000000
79
+ 2023-09-04 09:22:42,507 epoch 1 - iter 292/738 - loss 1.21016554 - time (sec): 53.28 - samples/sec: 1259.62 - lr: 0.000012 - momentum: 0.000000
80
+ 2023-09-04 09:22:55,118 epoch 1 - iter 365/738 - loss 1.04663956 - time (sec): 65.89 - samples/sec: 1260.56 - lr: 0.000015 - momentum: 0.000000
81
+ 2023-09-04 09:23:07,815 epoch 1 - iter 438/738 - loss 0.92994820 - time (sec): 78.59 - samples/sec: 1253.63 - lr: 0.000018 - momentum: 0.000000
82
+ 2023-09-04 09:23:19,538 epoch 1 - iter 511/738 - loss 0.84238109 - time (sec): 90.31 - samples/sec: 1259.18 - lr: 0.000021 - momentum: 0.000000
83
+ 2023-09-04 09:23:32,328 epoch 1 - iter 584/738 - loss 0.76960934 - time (sec): 103.10 - samples/sec: 1250.55 - lr: 0.000024 - momentum: 0.000000
84
+ 2023-09-04 09:23:48,644 epoch 1 - iter 657/738 - loss 0.69414930 - time (sec): 119.42 - samples/sec: 1241.21 - lr: 0.000027 - momentum: 0.000000
85
+ 2023-09-04 09:24:02,934 epoch 1 - iter 730/738 - loss 0.64412765 - time (sec): 133.71 - samples/sec: 1233.14 - lr: 0.000030 - momentum: 0.000000
86
+ 2023-09-04 09:24:04,079 ----------------------------------------------------------------------------------------------------
87
+ 2023-09-04 09:24:04,079 EPOCH 1 done: loss 0.6392 - lr: 0.000030
88
+ 2023-09-04 09:24:17,932 DEV : loss 0.15471519529819489 - f1-score (micro avg) 0.6791
89
+ 2023-09-04 09:24:17,960 saving best model
90
+ 2023-09-04 09:24:18,493 ----------------------------------------------------------------------------------------------------
91
+ 2023-09-04 09:24:32,866 epoch 2 - iter 73/738 - loss 0.16761812 - time (sec): 14.37 - samples/sec: 1177.44 - lr: 0.000030 - momentum: 0.000000
92
+ 2023-09-04 09:24:44,419 epoch 2 - iter 146/738 - loss 0.15138292 - time (sec): 25.92 - samples/sec: 1199.23 - lr: 0.000029 - momentum: 0.000000
93
+ 2023-09-04 09:24:58,295 epoch 2 - iter 219/738 - loss 0.14883077 - time (sec): 39.80 - samples/sec: 1166.13 - lr: 0.000029 - momentum: 0.000000
94
+ 2023-09-04 09:25:12,114 epoch 2 - iter 292/738 - loss 0.14741815 - time (sec): 53.62 - samples/sec: 1168.67 - lr: 0.000029 - momentum: 0.000000
95
+ 2023-09-04 09:25:23,984 epoch 2 - iter 365/738 - loss 0.14242339 - time (sec): 65.49 - samples/sec: 1190.67 - lr: 0.000028 - momentum: 0.000000
96
+ 2023-09-04 09:25:42,603 epoch 2 - iter 438/738 - loss 0.14100333 - time (sec): 84.11 - samples/sec: 1186.26 - lr: 0.000028 - momentum: 0.000000
97
+ 2023-09-04 09:25:56,405 epoch 2 - iter 511/738 - loss 0.13621877 - time (sec): 97.91 - samples/sec: 1185.38 - lr: 0.000028 - momentum: 0.000000
98
+ 2023-09-04 09:26:10,572 epoch 2 - iter 584/738 - loss 0.13365880 - time (sec): 112.08 - samples/sec: 1181.94 - lr: 0.000027 - momentum: 0.000000
99
+ 2023-09-04 09:26:24,017 epoch 2 - iter 657/738 - loss 0.13071969 - time (sec): 125.52 - samples/sec: 1187.22 - lr: 0.000027 - momentum: 0.000000
100
+ 2023-09-04 09:26:36,319 epoch 2 - iter 730/738 - loss 0.12695473 - time (sec): 137.82 - samples/sec: 1195.22 - lr: 0.000027 - momentum: 0.000000
101
+ 2023-09-04 09:26:37,592 ----------------------------------------------------------------------------------------------------
102
+ 2023-09-04 09:26:37,593 EPOCH 2 done: loss 0.1268 - lr: 0.000027
103
+ 2023-09-04 09:26:55,168 DEV : loss 0.09993426501750946 - f1-score (micro avg) 0.8006
104
+ 2023-09-04 09:26:55,196 saving best model
105
+ 2023-09-04 09:26:56,589 ----------------------------------------------------------------------------------------------------
106
+ 2023-09-04 09:27:09,231 epoch 3 - iter 73/738 - loss 0.07219142 - time (sec): 12.64 - samples/sec: 1202.50 - lr: 0.000026 - momentum: 0.000000
107
+ 2023-09-04 09:27:21,978 epoch 3 - iter 146/738 - loss 0.06633822 - time (sec): 25.39 - samples/sec: 1229.25 - lr: 0.000026 - momentum: 0.000000
108
+ 2023-09-04 09:27:35,312 epoch 3 - iter 219/738 - loss 0.07341871 - time (sec): 38.72 - samples/sec: 1248.01 - lr: 0.000026 - momentum: 0.000000
109
+ 2023-09-04 09:27:49,923 epoch 3 - iter 292/738 - loss 0.07149973 - time (sec): 53.33 - samples/sec: 1226.73 - lr: 0.000025 - momentum: 0.000000
110
+ 2023-09-04 09:28:04,553 epoch 3 - iter 365/738 - loss 0.07590859 - time (sec): 67.96 - samples/sec: 1220.19 - lr: 0.000025 - momentum: 0.000000
111
+ 2023-09-04 09:28:17,129 epoch 3 - iter 438/738 - loss 0.07660137 - time (sec): 80.54 - samples/sec: 1215.15 - lr: 0.000025 - momentum: 0.000000
112
+ 2023-09-04 09:28:30,282 epoch 3 - iter 511/738 - loss 0.07462537 - time (sec): 93.69 - samples/sec: 1220.33 - lr: 0.000024 - momentum: 0.000000
113
+ 2023-09-04 09:28:45,587 epoch 3 - iter 584/738 - loss 0.07483860 - time (sec): 109.00 - samples/sec: 1204.81 - lr: 0.000024 - momentum: 0.000000
114
+ 2023-09-04 09:28:58,611 epoch 3 - iter 657/738 - loss 0.07372653 - time (sec): 122.02 - samples/sec: 1210.81 - lr: 0.000024 - momentum: 0.000000
115
+ 2023-09-04 09:29:13,916 epoch 3 - iter 730/738 - loss 0.07209632 - time (sec): 137.33 - samples/sec: 1200.40 - lr: 0.000023 - momentum: 0.000000
116
+ 2023-09-04 09:29:15,048 ----------------------------------------------------------------------------------------------------
117
+ 2023-09-04 09:29:15,048 EPOCH 3 done: loss 0.0718 - lr: 0.000023
118
+ 2023-09-04 09:29:32,616 DEV : loss 0.12221837788820267 - f1-score (micro avg) 0.7872
119
+ 2023-09-04 09:29:32,643 ----------------------------------------------------------------------------------------------------
120
+ 2023-09-04 09:29:45,534 epoch 4 - iter 73/738 - loss 0.04605034 - time (sec): 12.89 - samples/sec: 1222.35 - lr: 0.000023 - momentum: 0.000000
121
+ 2023-09-04 09:30:00,070 epoch 4 - iter 146/738 - loss 0.04545629 - time (sec): 27.43 - samples/sec: 1218.80 - lr: 0.000023 - momentum: 0.000000
122
+ 2023-09-04 09:30:15,983 epoch 4 - iter 219/738 - loss 0.04527061 - time (sec): 43.34 - samples/sec: 1201.40 - lr: 0.000022 - momentum: 0.000000
123
+ 2023-09-04 09:30:28,926 epoch 4 - iter 292/738 - loss 0.04515660 - time (sec): 56.28 - samples/sec: 1197.89 - lr: 0.000022 - momentum: 0.000000
124
+ 2023-09-04 09:30:41,794 epoch 4 - iter 365/738 - loss 0.04704352 - time (sec): 69.15 - samples/sec: 1199.68 - lr: 0.000022 - momentum: 0.000000
125
+ 2023-09-04 09:30:53,662 epoch 4 - iter 438/738 - loss 0.04484810 - time (sec): 81.02 - samples/sec: 1203.52 - lr: 0.000021 - momentum: 0.000000
126
+ 2023-09-04 09:31:08,362 epoch 4 - iter 511/738 - loss 0.04374676 - time (sec): 95.72 - samples/sec: 1202.56 - lr: 0.000021 - momentum: 0.000000
127
+ 2023-09-04 09:31:21,314 epoch 4 - iter 584/738 - loss 0.04477741 - time (sec): 108.67 - samples/sec: 1200.04 - lr: 0.000021 - momentum: 0.000000
128
+ 2023-09-04 09:31:36,680 epoch 4 - iter 657/738 - loss 0.04576821 - time (sec): 124.04 - samples/sec: 1195.67 - lr: 0.000020 - momentum: 0.000000
129
+ 2023-09-04 09:31:49,911 epoch 4 - iter 730/738 - loss 0.04705550 - time (sec): 137.27 - samples/sec: 1201.61 - lr: 0.000020 - momentum: 0.000000
130
+ 2023-09-04 09:31:51,055 ----------------------------------------------------------------------------------------------------
131
+ 2023-09-04 09:31:51,055 EPOCH 4 done: loss 0.0467 - lr: 0.000020
132
+ 2023-09-04 09:32:08,573 DEV : loss 0.16911198198795319 - f1-score (micro avg) 0.7936
133
+ 2023-09-04 09:32:08,611 ----------------------------------------------------------------------------------------------------
134
+ 2023-09-04 09:32:21,105 epoch 5 - iter 73/738 - loss 0.04520474 - time (sec): 12.49 - samples/sec: 1229.86 - lr: 0.000020 - momentum: 0.000000
135
+ 2023-09-04 09:32:34,520 epoch 5 - iter 146/738 - loss 0.03466443 - time (sec): 25.91 - samples/sec: 1206.51 - lr: 0.000019 - momentum: 0.000000
136
+ 2023-09-04 09:32:49,136 epoch 5 - iter 219/738 - loss 0.03752519 - time (sec): 40.52 - samples/sec: 1198.98 - lr: 0.000019 - momentum: 0.000000
137
+ 2023-09-04 09:33:02,348 epoch 5 - iter 292/738 - loss 0.03479428 - time (sec): 53.74 - samples/sec: 1190.30 - lr: 0.000019 - momentum: 0.000000
138
+ 2023-09-04 09:33:17,938 epoch 5 - iter 365/738 - loss 0.03283963 - time (sec): 69.33 - samples/sec: 1174.82 - lr: 0.000018 - momentum: 0.000000
139
+ 2023-09-04 09:33:32,496 epoch 5 - iter 438/738 - loss 0.03344278 - time (sec): 83.88 - samples/sec: 1172.97 - lr: 0.000018 - momentum: 0.000000
140
+ 2023-09-04 09:33:45,386 epoch 5 - iter 511/738 - loss 0.03258506 - time (sec): 96.77 - samples/sec: 1176.67 - lr: 0.000018 - momentum: 0.000000
141
+ 2023-09-04 09:33:58,800 epoch 5 - iter 584/738 - loss 0.03255792 - time (sec): 110.19 - samples/sec: 1179.17 - lr: 0.000017 - momentum: 0.000000
142
+ 2023-09-04 09:34:14,334 epoch 5 - iter 657/738 - loss 0.03302122 - time (sec): 125.72 - samples/sec: 1180.32 - lr: 0.000017 - momentum: 0.000000
143
+ 2023-09-04 09:34:27,586 epoch 5 - iter 730/738 - loss 0.03398325 - time (sec): 138.97 - samples/sec: 1187.29 - lr: 0.000017 - momentum: 0.000000
144
+ 2023-09-04 09:34:28,742 ----------------------------------------------------------------------------------------------------
145
+ 2023-09-04 09:34:28,742 EPOCH 5 done: loss 0.0343 - lr: 0.000017
146
+ 2023-09-04 09:34:46,304 DEV : loss 0.16836385428905487 - f1-score (micro avg) 0.8079
147
+ 2023-09-04 09:34:46,331 saving best model
148
+ 2023-09-04 09:34:47,694 ----------------------------------------------------------------------------------------------------
149
+ 2023-09-04 09:35:00,156 epoch 6 - iter 73/738 - loss 0.03138358 - time (sec): 12.46 - samples/sec: 1198.91 - lr: 0.000016 - momentum: 0.000000
150
+ 2023-09-04 09:35:12,874 epoch 6 - iter 146/738 - loss 0.02510301 - time (sec): 25.18 - samples/sec: 1204.86 - lr: 0.000016 - momentum: 0.000000
151
+ 2023-09-04 09:35:29,204 epoch 6 - iter 219/738 - loss 0.02223835 - time (sec): 41.51 - samples/sec: 1184.25 - lr: 0.000016 - momentum: 0.000000
152
+ 2023-09-04 09:35:43,343 epoch 6 - iter 292/738 - loss 0.02522528 - time (sec): 55.65 - samples/sec: 1169.31 - lr: 0.000015 - momentum: 0.000000
153
+ 2023-09-04 09:35:56,430 epoch 6 - iter 365/738 - loss 0.02529903 - time (sec): 68.74 - samples/sec: 1178.35 - lr: 0.000015 - momentum: 0.000000
154
+ 2023-09-04 09:36:11,738 epoch 6 - iter 438/738 - loss 0.02531622 - time (sec): 84.04 - samples/sec: 1178.44 - lr: 0.000015 - momentum: 0.000000
155
+ 2023-09-04 09:36:24,220 epoch 6 - iter 511/738 - loss 0.02460370 - time (sec): 96.53 - samples/sec: 1182.78 - lr: 0.000014 - momentum: 0.000000
156
+ 2023-09-04 09:36:37,585 epoch 6 - iter 584/738 - loss 0.02425742 - time (sec): 109.89 - samples/sec: 1184.28 - lr: 0.000014 - momentum: 0.000000
157
+ 2023-09-04 09:36:53,465 epoch 6 - iter 657/738 - loss 0.02382671 - time (sec): 125.77 - samples/sec: 1185.39 - lr: 0.000014 - momentum: 0.000000
158
+ 2023-09-04 09:37:06,667 epoch 6 - iter 730/738 - loss 0.02374947 - time (sec): 138.97 - samples/sec: 1186.10 - lr: 0.000013 - momentum: 0.000000
159
+ 2023-09-04 09:37:07,907 ----------------------------------------------------------------------------------------------------
160
+ 2023-09-04 09:37:07,907 EPOCH 6 done: loss 0.0238 - lr: 0.000013
161
+ 2023-09-04 09:37:25,458 DEV : loss 0.17327718436717987 - f1-score (micro avg) 0.8246
162
+ 2023-09-04 09:37:25,486 saving best model
163
+ 2023-09-04 09:37:26,877 ----------------------------------------------------------------------------------------------------
164
+ 2023-09-04 09:37:39,400 epoch 7 - iter 73/738 - loss 0.01797924 - time (sec): 12.52 - samples/sec: 1214.04 - lr: 0.000013 - momentum: 0.000000
165
+ 2023-09-04 09:37:54,864 epoch 7 - iter 146/738 - loss 0.01908055 - time (sec): 27.99 - samples/sec: 1201.77 - lr: 0.000013 - momentum: 0.000000
166
+ 2023-09-04 09:38:06,657 epoch 7 - iter 219/738 - loss 0.01920515 - time (sec): 39.78 - samples/sec: 1224.36 - lr: 0.000012 - momentum: 0.000000
167
+ 2023-09-04 09:38:21,336 epoch 7 - iter 292/738 - loss 0.01810161 - time (sec): 54.46 - samples/sec: 1189.52 - lr: 0.000012 - momentum: 0.000000
168
+ 2023-09-04 09:38:36,027 epoch 7 - iter 365/738 - loss 0.01787997 - time (sec): 69.15 - samples/sec: 1182.81 - lr: 0.000012 - momentum: 0.000000
169
+ 2023-09-04 09:38:50,313 epoch 7 - iter 438/738 - loss 0.01966787 - time (sec): 83.43 - samples/sec: 1196.60 - lr: 0.000011 - momentum: 0.000000
170
+ 2023-09-04 09:39:05,223 epoch 7 - iter 511/738 - loss 0.01946810 - time (sec): 98.34 - samples/sec: 1193.45 - lr: 0.000011 - momentum: 0.000000
171
+ 2023-09-04 09:39:20,188 epoch 7 - iter 584/738 - loss 0.01863273 - time (sec): 113.31 - samples/sec: 1184.38 - lr: 0.000011 - momentum: 0.000000
172
+ 2023-09-04 09:39:32,825 epoch 7 - iter 657/738 - loss 0.01833497 - time (sec): 125.95 - samples/sec: 1182.71 - lr: 0.000010 - momentum: 0.000000
173
+ 2023-09-04 09:39:45,978 epoch 7 - iter 730/738 - loss 0.01845157 - time (sec): 139.10 - samples/sec: 1182.89 - lr: 0.000010 - momentum: 0.000000
174
+ 2023-09-04 09:39:47,290 ----------------------------------------------------------------------------------------------------
175
+ 2023-09-04 09:39:47,290 EPOCH 7 done: loss 0.0185 - lr: 0.000010
176
+ 2023-09-04 09:40:05,104 DEV : loss 0.18193018436431885 - f1-score (micro avg) 0.8247
177
+ 2023-09-04 09:40:05,132 saving best model
178
+ 2023-09-04 09:40:06,536 ----------------------------------------------------------------------------------------------------
179
+ 2023-09-04 09:40:19,868 epoch 8 - iter 73/738 - loss 0.01025211 - time (sec): 13.33 - samples/sec: 1212.36 - lr: 0.000010 - momentum: 0.000000
180
+ 2023-09-04 09:40:32,913 epoch 8 - iter 146/738 - loss 0.01132886 - time (sec): 26.38 - samples/sec: 1210.70 - lr: 0.000009 - momentum: 0.000000
181
+ 2023-09-04 09:40:47,183 epoch 8 - iter 219/738 - loss 0.01230033 - time (sec): 40.65 - samples/sec: 1220.66 - lr: 0.000009 - momentum: 0.000000
182
+ 2023-09-04 09:41:01,224 epoch 8 - iter 292/738 - loss 0.01353311 - time (sec): 54.69 - samples/sec: 1195.37 - lr: 0.000009 - momentum: 0.000000
183
+ 2023-09-04 09:41:14,252 epoch 8 - iter 365/738 - loss 0.01399786 - time (sec): 67.71 - samples/sec: 1196.02 - lr: 0.000008 - momentum: 0.000000
184
+ 2023-09-04 09:41:28,819 epoch 8 - iter 438/738 - loss 0.01395506 - time (sec): 82.28 - samples/sec: 1181.80 - lr: 0.000008 - momentum: 0.000000
185
+ 2023-09-04 09:41:41,664 epoch 8 - iter 511/738 - loss 0.01344088 - time (sec): 95.13 - samples/sec: 1183.18 - lr: 0.000008 - momentum: 0.000000
186
+ 2023-09-04 09:41:57,831 epoch 8 - iter 584/738 - loss 0.01376270 - time (sec): 111.29 - samples/sec: 1176.44 - lr: 0.000007 - momentum: 0.000000
187
+ 2023-09-04 09:42:10,940 epoch 8 - iter 657/738 - loss 0.01348014 - time (sec): 124.40 - samples/sec: 1180.41 - lr: 0.000007 - momentum: 0.000000
188
+ 2023-09-04 09:42:26,173 epoch 8 - iter 730/738 - loss 0.01291407 - time (sec): 139.64 - samples/sec: 1180.80 - lr: 0.000007 - momentum: 0.000000
189
+ 2023-09-04 09:42:27,393 ----------------------------------------------------------------------------------------------------
190
+ 2023-09-04 09:42:27,393 EPOCH 8 done: loss 0.0128 - lr: 0.000007
191
+ 2023-09-04 09:42:45,195 DEV : loss 0.19158028066158295 - f1-score (micro avg) 0.8259
192
+ 2023-09-04 09:42:45,223 saving best model
193
+ 2023-09-04 09:42:46,627 ----------------------------------------------------------------------------------------------------
194
+ 2023-09-04 09:43:00,701 epoch 9 - iter 73/738 - loss 0.01103610 - time (sec): 14.07 - samples/sec: 1231.02 - lr: 0.000006 - momentum: 0.000000
195
+ 2023-09-04 09:43:14,196 epoch 9 - iter 146/738 - loss 0.00967172 - time (sec): 27.57 - samples/sec: 1220.20 - lr: 0.000006 - momentum: 0.000000
196
+ 2023-09-04 09:43:28,417 epoch 9 - iter 219/738 - loss 0.01210302 - time (sec): 41.79 - samples/sec: 1181.64 - lr: 0.000006 - momentum: 0.000000
197
+ 2023-09-04 09:43:42,277 epoch 9 - iter 292/738 - loss 0.01072559 - time (sec): 55.65 - samples/sec: 1176.17 - lr: 0.000005 - momentum: 0.000000
198
+ 2023-09-04 09:43:56,106 epoch 9 - iter 365/738 - loss 0.01006460 - time (sec): 69.48 - samples/sec: 1174.04 - lr: 0.000005 - momentum: 0.000000
199
+ 2023-09-04 09:44:08,825 epoch 9 - iter 438/738 - loss 0.00985966 - time (sec): 82.20 - samples/sec: 1179.66 - lr: 0.000005 - momentum: 0.000000
200
+ 2023-09-04 09:44:22,290 epoch 9 - iter 511/738 - loss 0.00970238 - time (sec): 95.66 - samples/sec: 1193.45 - lr: 0.000004 - momentum: 0.000000
201
+ 2023-09-04 09:44:37,658 epoch 9 - iter 584/738 - loss 0.00995580 - time (sec): 111.03 - samples/sec: 1183.03 - lr: 0.000004 - momentum: 0.000000
202
+ 2023-09-04 09:44:51,538 epoch 9 - iter 657/738 - loss 0.01056869 - time (sec): 124.91 - samples/sec: 1184.22 - lr: 0.000004 - momentum: 0.000000
203
+ 2023-09-04 09:45:05,148 epoch 9 - iter 730/738 - loss 0.01067224 - time (sec): 138.52 - samples/sec: 1186.38 - lr: 0.000003 - momentum: 0.000000
204
+ 2023-09-04 09:45:07,069 ----------------------------------------------------------------------------------------------------
205
+ 2023-09-04 09:45:07,069 EPOCH 9 done: loss 0.0105 - lr: 0.000003
206
+ 2023-09-04 09:45:24,919 DEV : loss 0.19501827657222748 - f1-score (micro avg) 0.8267
207
+ 2023-09-04 09:45:24,946 saving best model
208
+ 2023-09-04 09:45:26,336 ----------------------------------------------------------------------------------------------------
209
+ 2023-09-04 09:45:39,933 epoch 10 - iter 73/738 - loss 0.01089305 - time (sec): 13.60 - samples/sec: 1185.75 - lr: 0.000003 - momentum: 0.000000
210
+ 2023-09-04 09:45:56,561 epoch 10 - iter 146/738 - loss 0.00877497 - time (sec): 30.22 - samples/sec: 1171.91 - lr: 0.000003 - momentum: 0.000000
211
+ 2023-09-04 09:46:10,757 epoch 10 - iter 219/738 - loss 0.00933966 - time (sec): 44.42 - samples/sec: 1157.10 - lr: 0.000002 - momentum: 0.000000
212
+ 2023-09-04 09:46:22,786 epoch 10 - iter 292/738 - loss 0.00795691 - time (sec): 56.45 - samples/sec: 1183.37 - lr: 0.000002 - momentum: 0.000000
213
+ 2023-09-04 09:46:34,767 epoch 10 - iter 365/738 - loss 0.00721217 - time (sec): 68.43 - samples/sec: 1201.70 - lr: 0.000002 - momentum: 0.000000
214
+ 2023-09-04 09:46:47,125 epoch 10 - iter 438/738 - loss 0.00806769 - time (sec): 80.79 - samples/sec: 1205.64 - lr: 0.000001 - momentum: 0.000000
215
+ 2023-09-04 09:47:01,780 epoch 10 - iter 511/738 - loss 0.00760834 - time (sec): 95.44 - samples/sec: 1203.76 - lr: 0.000001 - momentum: 0.000000
216
+ 2023-09-04 09:47:15,578 epoch 10 - iter 584/738 - loss 0.00787828 - time (sec): 109.24 - samples/sec: 1199.16 - lr: 0.000001 - momentum: 0.000000
217
+ 2023-09-04 09:47:29,252 epoch 10 - iter 657/738 - loss 0.00757345 - time (sec): 122.91 - samples/sec: 1195.68 - lr: 0.000000 - momentum: 0.000000
218
+ 2023-09-04 09:47:44,880 epoch 10 - iter 730/738 - loss 0.00742896 - time (sec): 138.54 - samples/sec: 1190.99 - lr: 0.000000 - momentum: 0.000000
219
+ 2023-09-04 09:47:45,959 ----------------------------------------------------------------------------------------------------
220
+ 2023-09-04 09:47:45,960 EPOCH 10 done: loss 0.0074 - lr: 0.000000
221
+ 2023-09-04 09:48:03,734 DEV : loss 0.19744744896888733 - f1-score (micro avg) 0.827
222
+ 2023-09-04 09:48:03,762 saving best model
223
+ 2023-09-04 09:48:06,486 ----------------------------------------------------------------------------------------------------
224
+ 2023-09-04 09:48:06,487 Loading model from best epoch ...
225
+ 2023-09-04 09:48:08,807 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 09:48:23,545
227
+ Results:
228
+ - F-score (micro) 0.8056
229
+ - F-score (macro) 0.6959
230
+ - Accuracy 0.6957
231
+
232
+ By class:
233
+ precision recall f1-score support
234
+
235
+ loc 0.8705 0.8776 0.8741 858
236
+ pers 0.7589 0.8324 0.7940 537
237
+ org 0.5725 0.5985 0.5852 132
238
+ time 0.5000 0.5926 0.5424 54
239
+ prod 0.7143 0.6557 0.6838 61
240
+
241
+ micro avg 0.7891 0.8228 0.8056 1642
242
+ macro avg 0.6832 0.7114 0.6959 1642
243
+ weighted avg 0.7921 0.8228 0.8067 1642
244
+
245
+ 2023-09-04 09:48:23,545 ----------------------------------------------------------------------------------------------------
training_params.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "username": "stefan-it",
3
+ "project_name": "/tmp/model",
4
+ "data_path": "stefan-it/autotrain-flair-hipe2022-fr-hmbert",
5
+ "token": "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",
6
+ "script_path": "/home/stefan/Repositories/hmTEAMS/bench",
7
+ "env": {}
8
+ }