Upload ./training.log with huggingface_hub
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training.log
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1 |
+
2023-10-25 03:05:38,651 ----------------------------------------------------------------------------------------------------
|
2 |
+
2023-10-25 03:05:38,652 Model: "SequenceTagger(
|
3 |
+
(embeddings): TransformerWordEmbeddings(
|
4 |
+
(model): BertModel(
|
5 |
+
(embeddings): BertEmbeddings(
|
6 |
+
(word_embeddings): Embedding(64001, 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): 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 |
+
(1): BertLayer(
|
39 |
+
(attention): BertAttention(
|
40 |
+
(self): BertSelfAttention(
|
41 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
42 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
43 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
44 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
45 |
+
)
|
46 |
+
(output): BertSelfOutput(
|
47 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
48 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
49 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
50 |
+
)
|
51 |
+
)
|
52 |
+
(intermediate): BertIntermediate(
|
53 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
54 |
+
(intermediate_act_fn): GELUActivation()
|
55 |
+
)
|
56 |
+
(output): BertOutput(
|
57 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
58 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
59 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
60 |
+
)
|
61 |
+
)
|
62 |
+
(2): BertLayer(
|
63 |
+
(attention): BertAttention(
|
64 |
+
(self): BertSelfAttention(
|
65 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
66 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
67 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
68 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
69 |
+
)
|
70 |
+
(output): BertSelfOutput(
|
71 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
72 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
73 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
74 |
+
)
|
75 |
+
)
|
76 |
+
(intermediate): BertIntermediate(
|
77 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
78 |
+
(intermediate_act_fn): GELUActivation()
|
79 |
+
)
|
80 |
+
(output): BertOutput(
|
81 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
82 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
83 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
84 |
+
)
|
85 |
+
)
|
86 |
+
(3): BertLayer(
|
87 |
+
(attention): BertAttention(
|
88 |
+
(self): BertSelfAttention(
|
89 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
90 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
91 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
92 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
93 |
+
)
|
94 |
+
(output): BertSelfOutput(
|
95 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
96 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
97 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
98 |
+
)
|
99 |
+
)
|
100 |
+
(intermediate): BertIntermediate(
|
101 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
102 |
+
(intermediate_act_fn): GELUActivation()
|
103 |
+
)
|
104 |
+
(output): BertOutput(
|
105 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
106 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
107 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
108 |
+
)
|
109 |
+
)
|
110 |
+
(4): BertLayer(
|
111 |
+
(attention): BertAttention(
|
112 |
+
(self): BertSelfAttention(
|
113 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
114 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
115 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
116 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
117 |
+
)
|
118 |
+
(output): BertSelfOutput(
|
119 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
120 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
121 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
122 |
+
)
|
123 |
+
)
|
124 |
+
(intermediate): BertIntermediate(
|
125 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
126 |
+
(intermediate_act_fn): GELUActivation()
|
127 |
+
)
|
128 |
+
(output): BertOutput(
|
129 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
130 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
131 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
132 |
+
)
|
133 |
+
)
|
134 |
+
(5): BertLayer(
|
135 |
+
(attention): BertAttention(
|
136 |
+
(self): BertSelfAttention(
|
137 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
138 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
139 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
140 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
141 |
+
)
|
142 |
+
(output): BertSelfOutput(
|
143 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
144 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
145 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
146 |
+
)
|
147 |
+
)
|
148 |
+
(intermediate): BertIntermediate(
|
149 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
150 |
+
(intermediate_act_fn): GELUActivation()
|
151 |
+
)
|
152 |
+
(output): BertOutput(
|
153 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
154 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
155 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
156 |
+
)
|
157 |
+
)
|
158 |
+
(6): BertLayer(
|
159 |
+
(attention): BertAttention(
|
160 |
+
(self): BertSelfAttention(
|
161 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
162 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
163 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
164 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
165 |
+
)
|
166 |
+
(output): BertSelfOutput(
|
167 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
168 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
169 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
170 |
+
)
|
171 |
+
)
|
172 |
+
(intermediate): BertIntermediate(
|
173 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
174 |
+
(intermediate_act_fn): GELUActivation()
|
175 |
+
)
|
176 |
+
(output): BertOutput(
|
177 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
178 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
179 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
180 |
+
)
|
181 |
+
)
|
182 |
+
(7): BertLayer(
|
183 |
+
(attention): BertAttention(
|
184 |
+
(self): BertSelfAttention(
|
185 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
186 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
187 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
188 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
189 |
+
)
|
190 |
+
(output): BertSelfOutput(
|
191 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
192 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
193 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
194 |
+
)
|
195 |
+
)
|
196 |
+
(intermediate): BertIntermediate(
|
197 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
198 |
+
(intermediate_act_fn): GELUActivation()
|
199 |
+
)
|
200 |
+
(output): BertOutput(
|
201 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
202 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
203 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
204 |
+
)
|
205 |
+
)
|
206 |
+
(8): BertLayer(
|
207 |
+
(attention): BertAttention(
|
208 |
+
(self): BertSelfAttention(
|
209 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
210 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
211 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
212 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
213 |
+
)
|
214 |
+
(output): BertSelfOutput(
|
215 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
216 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
217 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
218 |
+
)
|
219 |
+
)
|
220 |
+
(intermediate): BertIntermediate(
|
221 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
222 |
+
(intermediate_act_fn): GELUActivation()
|
223 |
+
)
|
224 |
+
(output): BertOutput(
|
225 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
226 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
227 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
228 |
+
)
|
229 |
+
)
|
230 |
+
(9): BertLayer(
|
231 |
+
(attention): BertAttention(
|
232 |
+
(self): BertSelfAttention(
|
233 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
234 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
235 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
236 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
237 |
+
)
|
238 |
+
(output): BertSelfOutput(
|
239 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
240 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
241 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
242 |
+
)
|
243 |
+
)
|
244 |
+
(intermediate): BertIntermediate(
|
245 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
246 |
+
(intermediate_act_fn): GELUActivation()
|
247 |
+
)
|
248 |
+
(output): BertOutput(
|
249 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
250 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
251 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
252 |
+
)
|
253 |
+
)
|
254 |
+
(10): BertLayer(
|
255 |
+
(attention): BertAttention(
|
256 |
+
(self): BertSelfAttention(
|
257 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
258 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
259 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
260 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
261 |
+
)
|
262 |
+
(output): BertSelfOutput(
|
263 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
264 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
265 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
266 |
+
)
|
267 |
+
)
|
268 |
+
(intermediate): BertIntermediate(
|
269 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
270 |
+
(intermediate_act_fn): GELUActivation()
|
271 |
+
)
|
272 |
+
(output): BertOutput(
|
273 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
274 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
275 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
276 |
+
)
|
277 |
+
)
|
278 |
+
(11): BertLayer(
|
279 |
+
(attention): BertAttention(
|
280 |
+
(self): BertSelfAttention(
|
281 |
+
(query): Linear(in_features=768, out_features=768, bias=True)
|
282 |
+
(key): Linear(in_features=768, out_features=768, bias=True)
|
283 |
+
(value): Linear(in_features=768, out_features=768, bias=True)
|
284 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
285 |
+
)
|
286 |
+
(output): BertSelfOutput(
|
287 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
288 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
289 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
290 |
+
)
|
291 |
+
)
|
292 |
+
(intermediate): BertIntermediate(
|
293 |
+
(dense): Linear(in_features=768, out_features=3072, bias=True)
|
294 |
+
(intermediate_act_fn): GELUActivation()
|
295 |
+
)
|
296 |
+
(output): BertOutput(
|
297 |
+
(dense): Linear(in_features=3072, out_features=768, bias=True)
|
298 |
+
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
|
299 |
+
(dropout): Dropout(p=0.1, inplace=False)
|
300 |
+
)
|
301 |
+
)
|
302 |
+
)
|
303 |
+
)
|
304 |
+
(pooler): BertPooler(
|
305 |
+
(dense): Linear(in_features=768, out_features=768, bias=True)
|
306 |
+
(activation): Tanh()
|
307 |
+
)
|
308 |
+
)
|
309 |
+
)
|
310 |
+
(locked_dropout): LockedDropout(p=0.5)
|
311 |
+
(linear): Linear(in_features=768, out_features=13, bias=True)
|
312 |
+
(loss_function): CrossEntropyLoss()
|
313 |
+
)"
|
314 |
+
2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
|
315 |
+
2023-10-25 03:05:38,652 MultiCorpus: 5777 train + 722 dev + 723 test sentences
|
316 |
+
- NER_ICDAR_EUROPEANA Corpus: 5777 train + 722 dev + 723 test sentences - /home/ubuntu/.flair/datasets/ner_icdar_europeana/nl
|
317 |
+
2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
|
318 |
+
2023-10-25 03:05:38,652 Train: 5777 sentences
|
319 |
+
2023-10-25 03:05:38,652 (train_with_dev=False, train_with_test=False)
|
320 |
+
2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
|
321 |
+
2023-10-25 03:05:38,652 Training Params:
|
322 |
+
2023-10-25 03:05:38,652 - learning_rate: "3e-05"
|
323 |
+
2023-10-25 03:05:38,652 - mini_batch_size: "8"
|
324 |
+
2023-10-25 03:05:38,652 - max_epochs: "10"
|
325 |
+
2023-10-25 03:05:38,652 - shuffle: "True"
|
326 |
+
2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
|
327 |
+
2023-10-25 03:05:38,652 Plugins:
|
328 |
+
2023-10-25 03:05:38,652 - TensorboardLogger
|
329 |
+
2023-10-25 03:05:38,652 - LinearScheduler | warmup_fraction: '0.1'
|
330 |
+
2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
|
331 |
+
2023-10-25 03:05:38,652 Final evaluation on model from best epoch (best-model.pt)
|
332 |
+
2023-10-25 03:05:38,652 - metric: "('micro avg', 'f1-score')"
|
333 |
+
2023-10-25 03:05:38,652 ----------------------------------------------------------------------------------------------------
|
334 |
+
2023-10-25 03:05:38,652 Computation:
|
335 |
+
2023-10-25 03:05:38,653 - compute on device: cuda:0
|
336 |
+
2023-10-25 03:05:38,653 - embedding storage: none
|
337 |
+
2023-10-25 03:05:38,653 ----------------------------------------------------------------------------------------------------
|
338 |
+
2023-10-25 03:05:38,653 Model training base path: "hmbench-icdar/nl-dbmdz/bert-base-historic-multilingual-64k-td-cased-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5"
|
339 |
+
2023-10-25 03:05:38,653 ----------------------------------------------------------------------------------------------------
|
340 |
+
2023-10-25 03:05:38,653 ----------------------------------------------------------------------------------------------------
|
341 |
+
2023-10-25 03:05:38,653 Logging anything other than scalars to TensorBoard is currently not supported.
|
342 |
+
2023-10-25 03:05:47,361 epoch 1 - iter 72/723 - loss 1.99617329 - time (sec): 8.71 - samples/sec: 1924.42 - lr: 0.000003 - momentum: 0.000000
|
343 |
+
2023-10-25 03:05:55,310 epoch 1 - iter 144/723 - loss 1.16280577 - time (sec): 16.66 - samples/sec: 1978.56 - lr: 0.000006 - momentum: 0.000000
|
344 |
+
2023-10-25 03:06:03,657 epoch 1 - iter 216/723 - loss 0.84060964 - time (sec): 25.00 - samples/sec: 2007.83 - lr: 0.000009 - momentum: 0.000000
|
345 |
+
2023-10-25 03:06:12,528 epoch 1 - iter 288/723 - loss 0.66558228 - time (sec): 33.87 - samples/sec: 2021.42 - lr: 0.000012 - momentum: 0.000000
|
346 |
+
2023-10-25 03:06:20,701 epoch 1 - iter 360/723 - loss 0.56766766 - time (sec): 42.05 - samples/sec: 2024.88 - lr: 0.000015 - momentum: 0.000000
|
347 |
+
2023-10-25 03:06:29,450 epoch 1 - iter 432/723 - loss 0.49389313 - time (sec): 50.80 - samples/sec: 2042.64 - lr: 0.000018 - momentum: 0.000000
|
348 |
+
2023-10-25 03:06:38,106 epoch 1 - iter 504/723 - loss 0.44520564 - time (sec): 59.45 - samples/sec: 2046.47 - lr: 0.000021 - momentum: 0.000000
|
349 |
+
2023-10-25 03:06:46,545 epoch 1 - iter 576/723 - loss 0.40819214 - time (sec): 67.89 - samples/sec: 2045.50 - lr: 0.000024 - momentum: 0.000000
|
350 |
+
2023-10-25 03:06:55,306 epoch 1 - iter 648/723 - loss 0.37477357 - time (sec): 76.65 - samples/sec: 2054.01 - lr: 0.000027 - momentum: 0.000000
|
351 |
+
2023-10-25 03:07:04,267 epoch 1 - iter 720/723 - loss 0.35059174 - time (sec): 85.61 - samples/sec: 2050.77 - lr: 0.000030 - momentum: 0.000000
|
352 |
+
2023-10-25 03:07:04,591 ----------------------------------------------------------------------------------------------------
|
353 |
+
2023-10-25 03:07:04,591 EPOCH 1 done: loss 0.3498 - lr: 0.000030
|
354 |
+
2023-10-25 03:07:07,856 DEV : loss 0.12723077833652496 - f1-score (micro avg) 0.605
|
355 |
+
2023-10-25 03:07:07,867 saving best model
|
356 |
+
2023-10-25 03:07:08,331 ----------------------------------------------------------------------------------------------------
|
357 |
+
2023-10-25 03:07:16,780 epoch 2 - iter 72/723 - loss 0.12378288 - time (sec): 8.45 - samples/sec: 2025.34 - lr: 0.000030 - momentum: 0.000000
|
358 |
+
2023-10-25 03:07:25,019 epoch 2 - iter 144/723 - loss 0.10929827 - time (sec): 16.69 - samples/sec: 2057.29 - lr: 0.000029 - momentum: 0.000000
|
359 |
+
2023-10-25 03:07:33,376 epoch 2 - iter 216/723 - loss 0.10475478 - time (sec): 25.04 - samples/sec: 2064.54 - lr: 0.000029 - momentum: 0.000000
|
360 |
+
2023-10-25 03:07:41,993 epoch 2 - iter 288/723 - loss 0.10331239 - time (sec): 33.66 - samples/sec: 2056.30 - lr: 0.000029 - momentum: 0.000000
|
361 |
+
2023-10-25 03:07:50,632 epoch 2 - iter 360/723 - loss 0.09975351 - time (sec): 42.30 - samples/sec: 2045.34 - lr: 0.000028 - momentum: 0.000000
|
362 |
+
2023-10-25 03:07:59,130 epoch 2 - iter 432/723 - loss 0.09772803 - time (sec): 50.80 - samples/sec: 2043.69 - lr: 0.000028 - momentum: 0.000000
|
363 |
+
2023-10-25 03:08:07,642 epoch 2 - iter 504/723 - loss 0.09812027 - time (sec): 59.31 - samples/sec: 2041.26 - lr: 0.000028 - momentum: 0.000000
|
364 |
+
2023-10-25 03:08:16,048 epoch 2 - iter 576/723 - loss 0.09695368 - time (sec): 67.72 - samples/sec: 2044.92 - lr: 0.000027 - momentum: 0.000000
|
365 |
+
2023-10-25 03:08:25,214 epoch 2 - iter 648/723 - loss 0.09757941 - time (sec): 76.88 - samples/sec: 2041.28 - lr: 0.000027 - momentum: 0.000000
|
366 |
+
2023-10-25 03:08:34,573 epoch 2 - iter 720/723 - loss 0.09551261 - time (sec): 86.24 - samples/sec: 2036.63 - lr: 0.000027 - momentum: 0.000000
|
367 |
+
2023-10-25 03:08:34,936 ----------------------------------------------------------------------------------------------------
|
368 |
+
2023-10-25 03:08:34,936 EPOCH 2 done: loss 0.0956 - lr: 0.000027
|
369 |
+
2023-10-25 03:08:38,643 DEV : loss 0.08566790819168091 - f1-score (micro avg) 0.7745
|
370 |
+
2023-10-25 03:08:38,655 saving best model
|
371 |
+
2023-10-25 03:08:39,247 ----------------------------------------------------------------------------------------------------
|
372 |
+
2023-10-25 03:08:47,889 epoch 3 - iter 72/723 - loss 0.05916132 - time (sec): 8.64 - samples/sec: 1981.91 - lr: 0.000026 - momentum: 0.000000
|
373 |
+
2023-10-25 03:08:56,374 epoch 3 - iter 144/723 - loss 0.06201111 - time (sec): 17.13 - samples/sec: 2019.26 - lr: 0.000026 - momentum: 0.000000
|
374 |
+
2023-10-25 03:09:05,644 epoch 3 - iter 216/723 - loss 0.05888965 - time (sec): 26.40 - samples/sec: 2030.27 - lr: 0.000026 - momentum: 0.000000
|
375 |
+
2023-10-25 03:09:14,319 epoch 3 - iter 288/723 - loss 0.05851997 - time (sec): 35.07 - samples/sec: 2041.19 - lr: 0.000025 - momentum: 0.000000
|
376 |
+
2023-10-25 03:09:22,895 epoch 3 - iter 360/723 - loss 0.05764096 - time (sec): 43.65 - samples/sec: 2054.60 - lr: 0.000025 - momentum: 0.000000
|
377 |
+
2023-10-25 03:09:31,205 epoch 3 - iter 432/723 - loss 0.06016911 - time (sec): 51.96 - samples/sec: 2050.28 - lr: 0.000025 - momentum: 0.000000
|
378 |
+
2023-10-25 03:09:39,400 epoch 3 - iter 504/723 - loss 0.05918448 - time (sec): 60.15 - samples/sec: 2049.80 - lr: 0.000024 - momentum: 0.000000
|
379 |
+
2023-10-25 03:09:47,713 epoch 3 - iter 576/723 - loss 0.05969279 - time (sec): 68.46 - samples/sec: 2053.93 - lr: 0.000024 - momentum: 0.000000
|
380 |
+
2023-10-25 03:09:55,945 epoch 3 - iter 648/723 - loss 0.06014119 - time (sec): 76.70 - samples/sec: 2057.74 - lr: 0.000024 - momentum: 0.000000
|
381 |
+
2023-10-25 03:10:04,767 epoch 3 - iter 720/723 - loss 0.05977622 - time (sec): 85.52 - samples/sec: 2051.90 - lr: 0.000023 - momentum: 0.000000
|
382 |
+
2023-10-25 03:10:05,178 ----------------------------------------------------------------------------------------------------
|
383 |
+
2023-10-25 03:10:05,178 EPOCH 3 done: loss 0.0596 - lr: 0.000023
|
384 |
+
2023-10-25 03:10:08,906 DEV : loss 0.09248801320791245 - f1-score (micro avg) 0.8169
|
385 |
+
2023-10-25 03:10:08,918 saving best model
|
386 |
+
2023-10-25 03:10:09,504 ----------------------------------------------------------------------------------------------------
|
387 |
+
2023-10-25 03:10:17,921 epoch 4 - iter 72/723 - loss 0.03098176 - time (sec): 8.42 - samples/sec: 1998.29 - lr: 0.000023 - momentum: 0.000000
|
388 |
+
2023-10-25 03:10:26,602 epoch 4 - iter 144/723 - loss 0.03944408 - time (sec): 17.10 - samples/sec: 2035.54 - lr: 0.000023 - momentum: 0.000000
|
389 |
+
2023-10-25 03:10:35,744 epoch 4 - iter 216/723 - loss 0.04237264 - time (sec): 26.24 - samples/sec: 2032.29 - lr: 0.000022 - momentum: 0.000000
|
390 |
+
2023-10-25 03:10:45,044 epoch 4 - iter 288/723 - loss 0.04386620 - time (sec): 35.54 - samples/sec: 1999.13 - lr: 0.000022 - momentum: 0.000000
|
391 |
+
2023-10-25 03:10:53,659 epoch 4 - iter 360/723 - loss 0.04538680 - time (sec): 44.15 - samples/sec: 2010.14 - lr: 0.000022 - momentum: 0.000000
|
392 |
+
2023-10-25 03:11:01,969 epoch 4 - iter 432/723 - loss 0.04433392 - time (sec): 52.46 - samples/sec: 2009.08 - lr: 0.000021 - momentum: 0.000000
|
393 |
+
2023-10-25 03:11:10,884 epoch 4 - iter 504/723 - loss 0.04255925 - time (sec): 61.38 - samples/sec: 2023.36 - lr: 0.000021 - momentum: 0.000000
|
394 |
+
2023-10-25 03:11:19,328 epoch 4 - iter 576/723 - loss 0.04165806 - time (sec): 69.82 - samples/sec: 2025.62 - lr: 0.000021 - momentum: 0.000000
|
395 |
+
2023-10-25 03:11:27,196 epoch 4 - iter 648/723 - loss 0.04211832 - time (sec): 77.69 - samples/sec: 2031.28 - lr: 0.000020 - momentum: 0.000000
|
396 |
+
2023-10-25 03:11:35,625 epoch 4 - iter 720/723 - loss 0.04183929 - time (sec): 86.12 - samples/sec: 2042.24 - lr: 0.000020 - momentum: 0.000000
|
397 |
+
2023-10-25 03:11:35,879 ----------------------------------------------------------------------------------------------------
|
398 |
+
2023-10-25 03:11:35,880 EPOCH 4 done: loss 0.0418 - lr: 0.000020
|
399 |
+
2023-10-25 03:11:39,311 DEV : loss 0.10374626517295837 - f1-score (micro avg) 0.7998
|
400 |
+
2023-10-25 03:11:39,323 ----------------------------------------------------------------------------------------------------
|
401 |
+
2023-10-25 03:11:47,653 epoch 5 - iter 72/723 - loss 0.03199178 - time (sec): 8.33 - samples/sec: 2125.79 - lr: 0.000020 - momentum: 0.000000
|
402 |
+
2023-10-25 03:11:56,611 epoch 5 - iter 144/723 - loss 0.03213636 - time (sec): 17.29 - samples/sec: 2049.33 - lr: 0.000019 - momentum: 0.000000
|
403 |
+
2023-10-25 03:12:04,995 epoch 5 - iter 216/723 - loss 0.03115061 - time (sec): 25.67 - samples/sec: 2057.92 - lr: 0.000019 - momentum: 0.000000
|
404 |
+
2023-10-25 03:12:13,192 epoch 5 - iter 288/723 - loss 0.03097977 - time (sec): 33.87 - samples/sec: 2042.62 - lr: 0.000019 - momentum: 0.000000
|
405 |
+
2023-10-25 03:12:21,459 epoch 5 - iter 360/723 - loss 0.02861777 - time (sec): 42.14 - samples/sec: 2046.34 - lr: 0.000018 - momentum: 0.000000
|
406 |
+
2023-10-25 03:12:30,963 epoch 5 - iter 432/723 - loss 0.02807181 - time (sec): 51.64 - samples/sec: 2026.17 - lr: 0.000018 - momentum: 0.000000
|
407 |
+
2023-10-25 03:12:39,302 epoch 5 - iter 504/723 - loss 0.02949394 - time (sec): 59.98 - samples/sec: 2025.30 - lr: 0.000018 - momentum: 0.000000
|
408 |
+
2023-10-25 03:12:48,150 epoch 5 - iter 576/723 - loss 0.03091767 - time (sec): 68.83 - samples/sec: 2028.32 - lr: 0.000017 - momentum: 0.000000
|
409 |
+
2023-10-25 03:12:57,221 epoch 5 - iter 648/723 - loss 0.03041035 - time (sec): 77.90 - samples/sec: 2032.00 - lr: 0.000017 - momentum: 0.000000
|
410 |
+
2023-10-25 03:13:05,882 epoch 5 - iter 720/723 - loss 0.03003918 - time (sec): 86.56 - samples/sec: 2030.99 - lr: 0.000017 - momentum: 0.000000
|
411 |
+
2023-10-25 03:13:06,115 ----------------------------------------------------------------------------------------------------
|
412 |
+
2023-10-25 03:13:06,116 EPOCH 5 done: loss 0.0300 - lr: 0.000017
|
413 |
+
2023-10-25 03:13:09,854 DEV : loss 0.10400616377592087 - f1-score (micro avg) 0.8329
|
414 |
+
2023-10-25 03:13:09,866 saving best model
|
415 |
+
2023-10-25 03:13:10,453 ----------------------------------------------------------------------------------------------------
|
416 |
+
2023-10-25 03:13:19,082 epoch 6 - iter 72/723 - loss 0.02189838 - time (sec): 8.63 - samples/sec: 2088.50 - lr: 0.000016 - momentum: 0.000000
|
417 |
+
2023-10-25 03:13:26,772 epoch 6 - iter 144/723 - loss 0.02550434 - time (sec): 16.32 - samples/sec: 2089.07 - lr: 0.000016 - momentum: 0.000000
|
418 |
+
2023-10-25 03:13:35,285 epoch 6 - iter 216/723 - loss 0.02613427 - time (sec): 24.83 - samples/sec: 2096.33 - lr: 0.000016 - momentum: 0.000000
|
419 |
+
2023-10-25 03:13:44,917 epoch 6 - iter 288/723 - loss 0.02482502 - time (sec): 34.46 - samples/sec: 2066.30 - lr: 0.000015 - momentum: 0.000000
|
420 |
+
2023-10-25 03:13:53,581 epoch 6 - iter 360/723 - loss 0.02424077 - time (sec): 43.13 - samples/sec: 2064.26 - lr: 0.000015 - momentum: 0.000000
|
421 |
+
2023-10-25 03:14:02,495 epoch 6 - iter 432/723 - loss 0.02422193 - time (sec): 52.04 - samples/sec: 2054.09 - lr: 0.000015 - momentum: 0.000000
|
422 |
+
2023-10-25 03:14:11,533 epoch 6 - iter 504/723 - loss 0.02433358 - time (sec): 61.08 - samples/sec: 2039.82 - lr: 0.000014 - momentum: 0.000000
|
423 |
+
2023-10-25 03:14:19,993 epoch 6 - iter 576/723 - loss 0.02425909 - time (sec): 69.54 - samples/sec: 2039.67 - lr: 0.000014 - momentum: 0.000000
|
424 |
+
2023-10-25 03:14:27,983 epoch 6 - iter 648/723 - loss 0.02400181 - time (sec): 77.53 - samples/sec: 2042.61 - lr: 0.000014 - momentum: 0.000000
|
425 |
+
2023-10-25 03:14:37,020 epoch 6 - iter 720/723 - loss 0.02315851 - time (sec): 86.57 - samples/sec: 2030.27 - lr: 0.000013 - momentum: 0.000000
|
426 |
+
2023-10-25 03:14:37,321 ----------------------------------------------------------------------------------------------------
|
427 |
+
2023-10-25 03:14:37,322 EPOCH 6 done: loss 0.0233 - lr: 0.000013
|
428 |
+
2023-10-25 03:14:40,761 DEV : loss 0.1466233879327774 - f1-score (micro avg) 0.8271
|
429 |
+
2023-10-25 03:14:40,773 ----------------------------------------------------------------------------------------------------
|
430 |
+
2023-10-25 03:14:49,901 epoch 7 - iter 72/723 - loss 0.01604370 - time (sec): 9.13 - samples/sec: 1997.10 - lr: 0.000013 - momentum: 0.000000
|
431 |
+
2023-10-25 03:14:58,957 epoch 7 - iter 144/723 - loss 0.01674537 - time (sec): 18.18 - samples/sec: 2010.15 - lr: 0.000013 - momentum: 0.000000
|
432 |
+
2023-10-25 03:15:07,312 epoch 7 - iter 216/723 - loss 0.01646182 - time (sec): 26.54 - samples/sec: 2011.93 - lr: 0.000012 - momentum: 0.000000
|
433 |
+
2023-10-25 03:15:16,127 epoch 7 - iter 288/723 - loss 0.01562860 - time (sec): 35.35 - samples/sec: 2010.60 - lr: 0.000012 - momentum: 0.000000
|
434 |
+
2023-10-25 03:15:24,723 epoch 7 - iter 360/723 - loss 0.01653965 - time (sec): 43.95 - samples/sec: 2014.53 - lr: 0.000012 - momentum: 0.000000
|
435 |
+
2023-10-25 03:15:33,248 epoch 7 - iter 432/723 - loss 0.01654614 - time (sec): 52.47 - samples/sec: 2019.73 - lr: 0.000011 - momentum: 0.000000
|
436 |
+
2023-10-25 03:15:41,936 epoch 7 - iter 504/723 - loss 0.01772708 - time (sec): 61.16 - samples/sec: 2012.83 - lr: 0.000011 - momentum: 0.000000
|
437 |
+
2023-10-25 03:15:51,042 epoch 7 - iter 576/723 - loss 0.01751254 - time (sec): 70.27 - samples/sec: 2014.86 - lr: 0.000011 - momentum: 0.000000
|
438 |
+
2023-10-25 03:15:59,505 epoch 7 - iter 648/723 - loss 0.01722334 - time (sec): 78.73 - samples/sec: 2017.94 - lr: 0.000010 - momentum: 0.000000
|
439 |
+
2023-10-25 03:16:07,618 epoch 7 - iter 720/723 - loss 0.01748532 - time (sec): 86.84 - samples/sec: 2020.86 - lr: 0.000010 - momentum: 0.000000
|
440 |
+
2023-10-25 03:16:08,082 ----------------------------------------------------------------------------------------------------
|
441 |
+
2023-10-25 03:16:08,082 EPOCH 7 done: loss 0.0175 - lr: 0.000010
|
442 |
+
2023-10-25 03:16:11,526 DEV : loss 0.14099310338497162 - f1-score (micro avg) 0.8444
|
443 |
+
2023-10-25 03:16:11,538 saving best model
|
444 |
+
2023-10-25 03:16:12,132 ----------------------------------------------------------------------------------------------------
|
445 |
+
2023-10-25 03:16:20,471 epoch 8 - iter 72/723 - loss 0.02389593 - time (sec): 8.34 - samples/sec: 2029.40 - lr: 0.000010 - momentum: 0.000000
|
446 |
+
2023-10-25 03:16:28,527 epoch 8 - iter 144/723 - loss 0.01519053 - time (sec): 16.39 - samples/sec: 2070.04 - lr: 0.000009 - momentum: 0.000000
|
447 |
+
2023-10-25 03:16:36,685 epoch 8 - iter 216/723 - loss 0.01528692 - time (sec): 24.55 - samples/sec: 2055.74 - lr: 0.000009 - momentum: 0.000000
|
448 |
+
2023-10-25 03:16:45,368 epoch 8 - iter 288/723 - loss 0.01454248 - time (sec): 33.23 - samples/sec: 2030.79 - lr: 0.000009 - momentum: 0.000000
|
449 |
+
2023-10-25 03:16:53,990 epoch 8 - iter 360/723 - loss 0.01392144 - time (sec): 41.86 - samples/sec: 2028.37 - lr: 0.000008 - momentum: 0.000000
|
450 |
+
2023-10-25 03:17:02,707 epoch 8 - iter 432/723 - loss 0.01333604 - time (sec): 50.57 - samples/sec: 2028.92 - lr: 0.000008 - momentum: 0.000000
|
451 |
+
2023-10-25 03:17:11,757 epoch 8 - iter 504/723 - loss 0.01255700 - time (sec): 59.62 - samples/sec: 2012.47 - lr: 0.000008 - momentum: 0.000000
|
452 |
+
2023-10-25 03:17:20,313 epoch 8 - iter 576/723 - loss 0.01296982 - time (sec): 68.18 - samples/sec: 2017.55 - lr: 0.000007 - momentum: 0.000000
|
453 |
+
2023-10-25 03:17:28,983 epoch 8 - iter 648/723 - loss 0.01386353 - time (sec): 76.85 - samples/sec: 2033.29 - lr: 0.000007 - momentum: 0.000000
|
454 |
+
2023-10-25 03:17:38,555 epoch 8 - iter 720/723 - loss 0.01336908 - time (sec): 86.42 - samples/sec: 2032.31 - lr: 0.000007 - momentum: 0.000000
|
455 |
+
2023-10-25 03:17:38,804 ----------------------------------------------------------------------------------------------------
|
456 |
+
2023-10-25 03:17:38,805 EPOCH 8 done: loss 0.0134 - lr: 0.000007
|
457 |
+
2023-10-25 03:17:42,543 DEV : loss 0.16321606934070587 - f1-score (micro avg) 0.8331
|
458 |
+
2023-10-25 03:17:42,555 ----------------------------------------------------------------------------------------------------
|
459 |
+
2023-10-25 03:17:52,154 epoch 9 - iter 72/723 - loss 0.00928599 - time (sec): 9.60 - samples/sec: 1891.08 - lr: 0.000006 - momentum: 0.000000
|
460 |
+
2023-10-25 03:18:00,763 epoch 9 - iter 144/723 - loss 0.01114101 - time (sec): 18.21 - samples/sec: 1981.42 - lr: 0.000006 - momentum: 0.000000
|
461 |
+
2023-10-25 03:18:09,687 epoch 9 - iter 216/723 - loss 0.00982378 - time (sec): 27.13 - samples/sec: 2013.82 - lr: 0.000006 - momentum: 0.000000
|
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+
2023-10-25 03:18:18,372 epoch 9 - iter 288/723 - loss 0.00914376 - time (sec): 35.82 - samples/sec: 2021.99 - lr: 0.000005 - momentum: 0.000000
|
463 |
+
2023-10-25 03:18:26,374 epoch 9 - iter 360/723 - loss 0.00876306 - time (sec): 43.82 - samples/sec: 2027.74 - lr: 0.000005 - momentum: 0.000000
|
464 |
+
2023-10-25 03:18:34,609 epoch 9 - iter 432/723 - loss 0.00823411 - time (sec): 52.05 - samples/sec: 2024.16 - lr: 0.000005 - momentum: 0.000000
|
465 |
+
2023-10-25 03:18:43,316 epoch 9 - iter 504/723 - loss 0.00818760 - time (sec): 60.76 - samples/sec: 2033.51 - lr: 0.000004 - momentum: 0.000000
|
466 |
+
2023-10-25 03:18:51,923 epoch 9 - iter 576/723 - loss 0.00821037 - time (sec): 69.37 - samples/sec: 2034.49 - lr: 0.000004 - momentum: 0.000000
|
467 |
+
2023-10-25 03:19:00,772 epoch 9 - iter 648/723 - loss 0.00872746 - time (sec): 78.22 - samples/sec: 2030.67 - lr: 0.000004 - momentum: 0.000000
|
468 |
+
2023-10-25 03:19:09,106 epoch 9 - iter 720/723 - loss 0.00885136 - time (sec): 86.55 - samples/sec: 2030.87 - lr: 0.000003 - momentum: 0.000000
|
469 |
+
2023-10-25 03:19:09,383 ----------------------------------------------------------------------------------------------------
|
470 |
+
2023-10-25 03:19:09,383 EPOCH 9 done: loss 0.0088 - lr: 0.000003
|
471 |
+
2023-10-25 03:19:13,170 DEV : loss 0.18107134103775024 - f1-score (micro avg) 0.829
|
472 |
+
2023-10-25 03:19:13,182 ----------------------------------------------------------------------------------------------------
|
473 |
+
2023-10-25 03:19:22,203 epoch 10 - iter 72/723 - loss 0.00646861 - time (sec): 9.02 - samples/sec: 1972.88 - lr: 0.000003 - momentum: 0.000000
|
474 |
+
2023-10-25 03:19:31,133 epoch 10 - iter 144/723 - loss 0.00638719 - time (sec): 17.95 - samples/sec: 1994.98 - lr: 0.000003 - momentum: 0.000000
|
475 |
+
2023-10-25 03:19:39,625 epoch 10 - iter 216/723 - loss 0.00729037 - time (sec): 26.44 - samples/sec: 1986.12 - lr: 0.000002 - momentum: 0.000000
|
476 |
+
2023-10-25 03:19:48,001 epoch 10 - iter 288/723 - loss 0.00646852 - time (sec): 34.82 - samples/sec: 1995.23 - lr: 0.000002 - momentum: 0.000000
|
477 |
+
2023-10-25 03:19:56,662 epoch 10 - iter 360/723 - loss 0.00687157 - time (sec): 43.48 - samples/sec: 1991.42 - lr: 0.000002 - momentum: 0.000000
|
478 |
+
2023-10-25 03:20:05,230 epoch 10 - iter 432/723 - loss 0.00636256 - time (sec): 52.05 - samples/sec: 2003.13 - lr: 0.000001 - momentum: 0.000000
|
479 |
+
2023-10-25 03:20:13,979 epoch 10 - iter 504/723 - loss 0.00637499 - time (sec): 60.80 - samples/sec: 1997.17 - lr: 0.000001 - momentum: 0.000000
|
480 |
+
2023-10-25 03:20:22,735 epoch 10 - iter 576/723 - loss 0.00672918 - time (sec): 69.55 - samples/sec: 1987.42 - lr: 0.000001 - momentum: 0.000000
|
481 |
+
2023-10-25 03:20:31,736 epoch 10 - iter 648/723 - loss 0.00714825 - time (sec): 78.55 - samples/sec: 1993.22 - lr: 0.000000 - momentum: 0.000000
|
482 |
+
2023-10-25 03:20:40,664 epoch 10 - iter 720/723 - loss 0.00697419 - time (sec): 87.48 - samples/sec: 2006.56 - lr: 0.000000 - momentum: 0.000000
|
483 |
+
2023-10-25 03:20:40,944 ----------------------------------------------------------------------------------------------------
|
484 |
+
2023-10-25 03:20:40,944 EPOCH 10 done: loss 0.0070 - lr: 0.000000
|
485 |
+
2023-10-25 03:20:44,389 DEV : loss 0.17600025236606598 - f1-score (micro avg) 0.8312
|
486 |
+
2023-10-25 03:20:44,876 ----------------------------------------------------------------------------------------------------
|
487 |
+
2023-10-25 03:20:44,876 Loading model from best epoch ...
|
488 |
+
2023-10-25 03:20:46,545 SequenceTagger predicts: Dictionary with 13 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
|
489 |
+
2023-10-25 03:20:50,083
|
490 |
+
Results:
|
491 |
+
- F-score (micro) 0.8055
|
492 |
+
- F-score (macro) 0.6854
|
493 |
+
- Accuracy 0.6868
|
494 |
+
|
495 |
+
By class:
|
496 |
+
precision recall f1-score support
|
497 |
+
|
498 |
+
PER 0.8254 0.8237 0.8245 482
|
499 |
+
LOC 0.9010 0.7948 0.8445 458
|
500 |
+
ORG 0.4364 0.3478 0.3871 69
|
501 |
+
|
502 |
+
micro avg 0.8351 0.7780 0.8055 1009
|
503 |
+
macro avg 0.7209 0.6554 0.6854 1009
|
504 |
+
weighted avg 0.8331 0.7780 0.8037 1009
|
505 |
+
|
506 |
+
2023-10-25 03:20:50,084 ----------------------------------------------------------------------------------------------------
|