End of training
Browse files- README.md +5 -5
- all_results.json +17 -0
- eval_results.json +12 -0
- predict_results.txt +539 -0
- train_results.json +8 -0
- trainer_state.json +192 -0
README.md
CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
|
|
20 |
|
21 |
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
|
22 |
It achieves the following results on the evaluation set:
|
23 |
-
- Loss: 0.
|
24 |
-
- Accuracy: 0.
|
25 |
-
- F1: 0.
|
26 |
-
- Precision: 0.
|
27 |
-
- Recall: 0.
|
28 |
|
29 |
## Model description
|
30 |
|
|
|
20 |
|
21 |
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
|
22 |
It achieves the following results on the evaluation set:
|
23 |
+
- Loss: 0.4720
|
24 |
+
- Accuracy: 0.8290
|
25 |
+
- F1: 0.6462
|
26 |
+
- Precision: 0.6667
|
27 |
+
- Recall: 0.6269
|
28 |
|
29 |
## Model description
|
30 |
|
all_results.json
ADDED
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 9.0,
|
3 |
+
"eval_accuracy": 0.828996282527881,
|
4 |
+
"eval_f1": 0.6461538461538462,
|
5 |
+
"eval_loss": 0.4720025956630707,
|
6 |
+
"eval_precision": 0.6666666666666666,
|
7 |
+
"eval_recall": 0.6268656716417911,
|
8 |
+
"eval_runtime": 2.1632,
|
9 |
+
"eval_samples": 268,
|
10 |
+
"eval_samples_per_second": 248.706,
|
11 |
+
"eval_steps_per_second": 4.161,
|
12 |
+
"train_loss": 0.2750625179312323,
|
13 |
+
"train_runtime": 261.0331,
|
14 |
+
"train_samples": 1878,
|
15 |
+
"train_samples_per_second": 719.449,
|
16 |
+
"train_steps_per_second": 22.602
|
17 |
+
}
|
eval_results.json
ADDED
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 9.0,
|
3 |
+
"eval_accuracy": 0.828996282527881,
|
4 |
+
"eval_f1": 0.6461538461538462,
|
5 |
+
"eval_loss": 0.4720025956630707,
|
6 |
+
"eval_precision": 0.6666666666666666,
|
7 |
+
"eval_recall": 0.6268656716417911,
|
8 |
+
"eval_runtime": 2.1632,
|
9 |
+
"eval_samples": 268,
|
10 |
+
"eval_samples_per_second": 248.706,
|
11 |
+
"eval_steps_per_second": 4.161
|
12 |
+
}
|
predict_results.txt
ADDED
@@ -0,0 +1,539 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
index prediction
|
2 |
+
0 1
|
3 |
+
1 0
|
4 |
+
2 0
|
5 |
+
3 1
|
6 |
+
4 0
|
7 |
+
5 0
|
8 |
+
6 1
|
9 |
+
7 1
|
10 |
+
8 1
|
11 |
+
9 0
|
12 |
+
10 0
|
13 |
+
11 0
|
14 |
+
12 1
|
15 |
+
13 0
|
16 |
+
14 0
|
17 |
+
15 1
|
18 |
+
16 1
|
19 |
+
17 0
|
20 |
+
18 0
|
21 |
+
19 0
|
22 |
+
20 0
|
23 |
+
21 0
|
24 |
+
22 0
|
25 |
+
23 1
|
26 |
+
24 0
|
27 |
+
25 0
|
28 |
+
26 1
|
29 |
+
27 1
|
30 |
+
28 0
|
31 |
+
29 0
|
32 |
+
30 0
|
33 |
+
31 0
|
34 |
+
32 0
|
35 |
+
33 1
|
36 |
+
34 0
|
37 |
+
35 0
|
38 |
+
36 0
|
39 |
+
37 0
|
40 |
+
38 0
|
41 |
+
39 0
|
42 |
+
40 0
|
43 |
+
41 0
|
44 |
+
42 0
|
45 |
+
43 0
|
46 |
+
44 0
|
47 |
+
45 0
|
48 |
+
46 0
|
49 |
+
47 0
|
50 |
+
48 0
|
51 |
+
49 0
|
52 |
+
50 0
|
53 |
+
51 0
|
54 |
+
52 0
|
55 |
+
53 0
|
56 |
+
54 0
|
57 |
+
55 0
|
58 |
+
56 0
|
59 |
+
57 0
|
60 |
+
58 0
|
61 |
+
59 0
|
62 |
+
60 0
|
63 |
+
61 0
|
64 |
+
62 0
|
65 |
+
63 0
|
66 |
+
64 0
|
67 |
+
65 0
|
68 |
+
66 1
|
69 |
+
67 0
|
70 |
+
68 0
|
71 |
+
69 0
|
72 |
+
70 1
|
73 |
+
71 0
|
74 |
+
72 0
|
75 |
+
73 1
|
76 |
+
74 0
|
77 |
+
75 1
|
78 |
+
76 0
|
79 |
+
77 1
|
80 |
+
78 0
|
81 |
+
79 0
|
82 |
+
80 0
|
83 |
+
81 1
|
84 |
+
82 0
|
85 |
+
83 1
|
86 |
+
84 0
|
87 |
+
85 0
|
88 |
+
86 0
|
89 |
+
87 0
|
90 |
+
88 0
|
91 |
+
89 0
|
92 |
+
90 0
|
93 |
+
91 0
|
94 |
+
92 0
|
95 |
+
93 0
|
96 |
+
94 0
|
97 |
+
95 0
|
98 |
+
96 1
|
99 |
+
97 1
|
100 |
+
98 0
|
101 |
+
99 0
|
102 |
+
100 0
|
103 |
+
101 0
|
104 |
+
102 0
|
105 |
+
103 0
|
106 |
+
104 0
|
107 |
+
105 1
|
108 |
+
106 0
|
109 |
+
107 0
|
110 |
+
108 1
|
111 |
+
109 0
|
112 |
+
110 0
|
113 |
+
111 1
|
114 |
+
112 0
|
115 |
+
113 0
|
116 |
+
114 0
|
117 |
+
115 0
|
118 |
+
116 0
|
119 |
+
117 1
|
120 |
+
118 0
|
121 |
+
119 1
|
122 |
+
120 0
|
123 |
+
121 0
|
124 |
+
122 0
|
125 |
+
123 0
|
126 |
+
124 1
|
127 |
+
125 0
|
128 |
+
126 0
|
129 |
+
127 0
|
130 |
+
128 0
|
131 |
+
129 0
|
132 |
+
130 1
|
133 |
+
131 0
|
134 |
+
132 0
|
135 |
+
133 0
|
136 |
+
134 0
|
137 |
+
135 0
|
138 |
+
136 0
|
139 |
+
137 0
|
140 |
+
138 0
|
141 |
+
139 0
|
142 |
+
140 1
|
143 |
+
141 0
|
144 |
+
142 1
|
145 |
+
143 0
|
146 |
+
144 1
|
147 |
+
145 0
|
148 |
+
146 0
|
149 |
+
147 1
|
150 |
+
148 0
|
151 |
+
149 0
|
152 |
+
150 0
|
153 |
+
151 0
|
154 |
+
152 0
|
155 |
+
153 0
|
156 |
+
154 0
|
157 |
+
155 0
|
158 |
+
156 0
|
159 |
+
157 0
|
160 |
+
158 0
|
161 |
+
159 0
|
162 |
+
160 0
|
163 |
+
161 0
|
164 |
+
162 0
|
165 |
+
163 1
|
166 |
+
164 0
|
167 |
+
165 0
|
168 |
+
166 0
|
169 |
+
167 0
|
170 |
+
168 0
|
171 |
+
169 0
|
172 |
+
170 0
|
173 |
+
171 1
|
174 |
+
172 0
|
175 |
+
173 0
|
176 |
+
174 1
|
177 |
+
175 0
|
178 |
+
176 1
|
179 |
+
177 0
|
180 |
+
178 0
|
181 |
+
179 0
|
182 |
+
180 1
|
183 |
+
181 0
|
184 |
+
182 0
|
185 |
+
183 0
|
186 |
+
184 0
|
187 |
+
185 1
|
188 |
+
186 1
|
189 |
+
187 1
|
190 |
+
188 0
|
191 |
+
189 0
|
192 |
+
190 0
|
193 |
+
191 0
|
194 |
+
192 0
|
195 |
+
193 1
|
196 |
+
194 0
|
197 |
+
195 1
|
198 |
+
196 0
|
199 |
+
197 0
|
200 |
+
198 1
|
201 |
+
199 0
|
202 |
+
200 0
|
203 |
+
201 0
|
204 |
+
202 1
|
205 |
+
203 1
|
206 |
+
204 0
|
207 |
+
205 1
|
208 |
+
206 0
|
209 |
+
207 0
|
210 |
+
208 0
|
211 |
+
209 0
|
212 |
+
210 0
|
213 |
+
211 0
|
214 |
+
212 0
|
215 |
+
213 0
|
216 |
+
214 0
|
217 |
+
215 0
|
218 |
+
216 1
|
219 |
+
217 0
|
220 |
+
218 1
|
221 |
+
219 0
|
222 |
+
220 0
|
223 |
+
221 0
|
224 |
+
222 0
|
225 |
+
223 0
|
226 |
+
224 0
|
227 |
+
225 0
|
228 |
+
226 1
|
229 |
+
227 0
|
230 |
+
228 1
|
231 |
+
229 0
|
232 |
+
230 0
|
233 |
+
231 1
|
234 |
+
232 0
|
235 |
+
233 0
|
236 |
+
234 0
|
237 |
+
235 0
|
238 |
+
236 0
|
239 |
+
237 1
|
240 |
+
238 0
|
241 |
+
239 0
|
242 |
+
240 1
|
243 |
+
241 0
|
244 |
+
242 0
|
245 |
+
243 0
|
246 |
+
244 0
|
247 |
+
245 0
|
248 |
+
246 1
|
249 |
+
247 0
|
250 |
+
248 0
|
251 |
+
249 1
|
252 |
+
250 0
|
253 |
+
251 1
|
254 |
+
252 0
|
255 |
+
253 1
|
256 |
+
254 0
|
257 |
+
255 0
|
258 |
+
256 0
|
259 |
+
257 1
|
260 |
+
258 0
|
261 |
+
259 0
|
262 |
+
260 1
|
263 |
+
261 0
|
264 |
+
262 0
|
265 |
+
263 0
|
266 |
+
264 0
|
267 |
+
265 0
|
268 |
+
266 1
|
269 |
+
267 0
|
270 |
+
268 0
|
271 |
+
269 0
|
272 |
+
270 0
|
273 |
+
271 1
|
274 |
+
272 0
|
275 |
+
273 1
|
276 |
+
274 0
|
277 |
+
275 0
|
278 |
+
276 0
|
279 |
+
277 0
|
280 |
+
278 1
|
281 |
+
279 0
|
282 |
+
280 0
|
283 |
+
281 0
|
284 |
+
282 1
|
285 |
+
283 0
|
286 |
+
284 0
|
287 |
+
285 0
|
288 |
+
286 0
|
289 |
+
287 0
|
290 |
+
288 0
|
291 |
+
289 0
|
292 |
+
290 0
|
293 |
+
291 0
|
294 |
+
292 1
|
295 |
+
293 0
|
296 |
+
294 1
|
297 |
+
295 0
|
298 |
+
296 0
|
299 |
+
297 1
|
300 |
+
298 1
|
301 |
+
299 0
|
302 |
+
300 0
|
303 |
+
301 1
|
304 |
+
302 0
|
305 |
+
303 0
|
306 |
+
304 0
|
307 |
+
305 0
|
308 |
+
306 1
|
309 |
+
307 0
|
310 |
+
308 1
|
311 |
+
309 0
|
312 |
+
310 0
|
313 |
+
311 0
|
314 |
+
312 0
|
315 |
+
313 0
|
316 |
+
314 0
|
317 |
+
315 0
|
318 |
+
316 0
|
319 |
+
317 1
|
320 |
+
318 0
|
321 |
+
319 0
|
322 |
+
320 1
|
323 |
+
321 1
|
324 |
+
322 0
|
325 |
+
323 0
|
326 |
+
324 0
|
327 |
+
325 0
|
328 |
+
326 1
|
329 |
+
327 0
|
330 |
+
328 0
|
331 |
+
329 0
|
332 |
+
330 1
|
333 |
+
331 1
|
334 |
+
332 0
|
335 |
+
333 0
|
336 |
+
334 0
|
337 |
+
335 0
|
338 |
+
336 0
|
339 |
+
337 0
|
340 |
+
338 0
|
341 |
+
339 0
|
342 |
+
340 1
|
343 |
+
341 1
|
344 |
+
342 0
|
345 |
+
343 0
|
346 |
+
344 0
|
347 |
+
345 0
|
348 |
+
346 1
|
349 |
+
347 0
|
350 |
+
348 0
|
351 |
+
349 0
|
352 |
+
350 0
|
353 |
+
351 0
|
354 |
+
352 0
|
355 |
+
353 1
|
356 |
+
354 0
|
357 |
+
355 0
|
358 |
+
356 0
|
359 |
+
357 0
|
360 |
+
358 0
|
361 |
+
359 1
|
362 |
+
360 0
|
363 |
+
361 0
|
364 |
+
362 1
|
365 |
+
363 0
|
366 |
+
364 0
|
367 |
+
365 0
|
368 |
+
366 1
|
369 |
+
367 0
|
370 |
+
368 1
|
371 |
+
369 0
|
372 |
+
370 0
|
373 |
+
371 1
|
374 |
+
372 0
|
375 |
+
373 0
|
376 |
+
374 0
|
377 |
+
375 0
|
378 |
+
376 0
|
379 |
+
377 0
|
380 |
+
378 1
|
381 |
+
379 1
|
382 |
+
380 1
|
383 |
+
381 0
|
384 |
+
382 0
|
385 |
+
383 0
|
386 |
+
384 1
|
387 |
+
385 0
|
388 |
+
386 0
|
389 |
+
387 0
|
390 |
+
388 1
|
391 |
+
389 0
|
392 |
+
390 1
|
393 |
+
391 0
|
394 |
+
392 1
|
395 |
+
393 0
|
396 |
+
394 1
|
397 |
+
395 0
|
398 |
+
396 0
|
399 |
+
397 0
|
400 |
+
398 1
|
401 |
+
399 0
|
402 |
+
400 0
|
403 |
+
401 0
|
404 |
+
402 0
|
405 |
+
403 1
|
406 |
+
404 0
|
407 |
+
405 1
|
408 |
+
406 0
|
409 |
+
407 1
|
410 |
+
408 0
|
411 |
+
409 0
|
412 |
+
410 0
|
413 |
+
411 0
|
414 |
+
412 1
|
415 |
+
413 0
|
416 |
+
414 0
|
417 |
+
415 0
|
418 |
+
416 0
|
419 |
+
417 0
|
420 |
+
418 1
|
421 |
+
419 0
|
422 |
+
420 1
|
423 |
+
421 0
|
424 |
+
422 1
|
425 |
+
423 1
|
426 |
+
424 0
|
427 |
+
425 0
|
428 |
+
426 0
|
429 |
+
427 0
|
430 |
+
428 0
|
431 |
+
429 0
|
432 |
+
430 0
|
433 |
+
431 1
|
434 |
+
432 0
|
435 |
+
433 0
|
436 |
+
434 1
|
437 |
+
435 0
|
438 |
+
436 1
|
439 |
+
437 0
|
440 |
+
438 0
|
441 |
+
439 0
|
442 |
+
440 0
|
443 |
+
441 1
|
444 |
+
442 0
|
445 |
+
443 0
|
446 |
+
444 0
|
447 |
+
445 0
|
448 |
+
446 1
|
449 |
+
447 0
|
450 |
+
448 0
|
451 |
+
449 1
|
452 |
+
450 0
|
453 |
+
451 1
|
454 |
+
452 0
|
455 |
+
453 0
|
456 |
+
454 0
|
457 |
+
455 0
|
458 |
+
456 1
|
459 |
+
457 0
|
460 |
+
458 0
|
461 |
+
459 0
|
462 |
+
460 0
|
463 |
+
461 0
|
464 |
+
462 0
|
465 |
+
463 1
|
466 |
+
464 0
|
467 |
+
465 0
|
468 |
+
466 0
|
469 |
+
467 0
|
470 |
+
468 0
|
471 |
+
469 1
|
472 |
+
470 1
|
473 |
+
471 1
|
474 |
+
472 0
|
475 |
+
473 0
|
476 |
+
474 0
|
477 |
+
475 1
|
478 |
+
476 0
|
479 |
+
477 0
|
480 |
+
478 0
|
481 |
+
479 0
|
482 |
+
480 1
|
483 |
+
481 0
|
484 |
+
482 0
|
485 |
+
483 0
|
486 |
+
484 0
|
487 |
+
485 0
|
488 |
+
486 1
|
489 |
+
487 1
|
490 |
+
488 0
|
491 |
+
489 0
|
492 |
+
490 0
|
493 |
+
491 0
|
494 |
+
492 0
|
495 |
+
493 0
|
496 |
+
494 1
|
497 |
+
495 1
|
498 |
+
496 0
|
499 |
+
497 0
|
500 |
+
498 0
|
501 |
+
499 0
|
502 |
+
500 0
|
503 |
+
501 0
|
504 |
+
502 0
|
505 |
+
503 0
|
506 |
+
504 0
|
507 |
+
505 0
|
508 |
+
506 0
|
509 |
+
507 0
|
510 |
+
508 0
|
511 |
+
509 0
|
512 |
+
510 0
|
513 |
+
511 0
|
514 |
+
512 0
|
515 |
+
513 0
|
516 |
+
514 0
|
517 |
+
515 0
|
518 |
+
516 0
|
519 |
+
517 0
|
520 |
+
518 1
|
521 |
+
519 1
|
522 |
+
520 0
|
523 |
+
521 0
|
524 |
+
522 0
|
525 |
+
523 0
|
526 |
+
524 0
|
527 |
+
525 0
|
528 |
+
526 0
|
529 |
+
527 0
|
530 |
+
528 0
|
531 |
+
529 0
|
532 |
+
530 1
|
533 |
+
531 0
|
534 |
+
532 0
|
535 |
+
533 0
|
536 |
+
534 0
|
537 |
+
535 0
|
538 |
+
536 1
|
539 |
+
537 1
|
train_results.json
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"epoch": 9.0,
|
3 |
+
"train_loss": 0.2750625179312323,
|
4 |
+
"train_runtime": 261.0331,
|
5 |
+
"train_samples": 1878,
|
6 |
+
"train_samples_per_second": 719.449,
|
7 |
+
"train_steps_per_second": 22.602
|
8 |
+
}
|
trainer_state.json
ADDED
@@ -0,0 +1,192 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"best_metric": 0.706896551724138,
|
3 |
+
"best_model_checkpoint": "outputs/bert-base-multilingual-cased-twitter-indonesia-sarcastic/checkpoint-354",
|
4 |
+
"epoch": 9.0,
|
5 |
+
"eval_steps": 500,
|
6 |
+
"global_step": 531,
|
7 |
+
"is_hyper_param_search": false,
|
8 |
+
"is_local_process_zero": true,
|
9 |
+
"is_world_process_zero": true,
|
10 |
+
"log_history": [
|
11 |
+
{
|
12 |
+
"epoch": 1.0,
|
13 |
+
"learning_rate": 9.99761572031246e-06,
|
14 |
+
"loss": 0.5333,
|
15 |
+
"step": 59
|
16 |
+
},
|
17 |
+
{
|
18 |
+
"epoch": 1.0,
|
19 |
+
"eval_accuracy": 0.75,
|
20 |
+
"eval_f1": 0.0,
|
21 |
+
"eval_loss": 0.47915688157081604,
|
22 |
+
"eval_precision": 0.0,
|
23 |
+
"eval_recall": 0.0,
|
24 |
+
"eval_runtime": 1.0725,
|
25 |
+
"eval_samples_per_second": 249.878,
|
26 |
+
"eval_steps_per_second": 4.662,
|
27 |
+
"step": 59
|
28 |
+
},
|
29 |
+
{
|
30 |
+
"epoch": 2.0,
|
31 |
+
"learning_rate": 9.990465155165683e-06,
|
32 |
+
"loss": 0.4642,
|
33 |
+
"step": 118
|
34 |
+
},
|
35 |
+
{
|
36 |
+
"epoch": 2.0,
|
37 |
+
"eval_accuracy": 0.7910447761194029,
|
38 |
+
"eval_f1": 0.3,
|
39 |
+
"eval_loss": 0.4418473243713379,
|
40 |
+
"eval_precision": 0.9230769230769231,
|
41 |
+
"eval_recall": 0.1791044776119403,
|
42 |
+
"eval_runtime": 1.0651,
|
43 |
+
"eval_samples_per_second": 251.625,
|
44 |
+
"eval_steps_per_second": 4.694,
|
45 |
+
"step": 118
|
46 |
+
},
|
47 |
+
{
|
48 |
+
"epoch": 3.0,
|
49 |
+
"learning_rate": 9.978555124138569e-06,
|
50 |
+
"loss": 0.3961,
|
51 |
+
"step": 177
|
52 |
+
},
|
53 |
+
{
|
54 |
+
"epoch": 3.0,
|
55 |
+
"eval_accuracy": 0.8134328358208955,
|
56 |
+
"eval_f1": 0.5192307692307693,
|
57 |
+
"eval_loss": 0.4319072365760803,
|
58 |
+
"eval_precision": 0.7297297297297297,
|
59 |
+
"eval_recall": 0.40298507462686567,
|
60 |
+
"eval_runtime": 1.0672,
|
61 |
+
"eval_samples_per_second": 251.127,
|
62 |
+
"eval_steps_per_second": 4.685,
|
63 |
+
"step": 177
|
64 |
+
},
|
65 |
+
{
|
66 |
+
"epoch": 4.0,
|
67 |
+
"learning_rate": 9.961568226140335e-06,
|
68 |
+
"loss": 0.325,
|
69 |
+
"step": 236
|
70 |
+
},
|
71 |
+
{
|
72 |
+
"epoch": 4.0,
|
73 |
+
"eval_accuracy": 0.746268656716418,
|
74 |
+
"eval_f1": 0.6179775280898877,
|
75 |
+
"eval_loss": 0.5264418721199036,
|
76 |
+
"eval_precision": 0.4954954954954955,
|
77 |
+
"eval_recall": 0.8208955223880597,
|
78 |
+
"eval_runtime": 1.0669,
|
79 |
+
"eval_samples_per_second": 251.206,
|
80 |
+
"eval_steps_per_second": 4.687,
|
81 |
+
"step": 236
|
82 |
+
},
|
83 |
+
{
|
84 |
+
"epoch": 5.0,
|
85 |
+
"learning_rate": 9.93968485932029e-06,
|
86 |
+
"loss": 0.2432,
|
87 |
+
"step": 295
|
88 |
+
},
|
89 |
+
{
|
90 |
+
"epoch": 5.0,
|
91 |
+
"eval_accuracy": 0.8246268656716418,
|
92 |
+
"eval_f1": 0.6299212598425197,
|
93 |
+
"eval_loss": 0.4624307453632355,
|
94 |
+
"eval_precision": 0.6666666666666666,
|
95 |
+
"eval_recall": 0.5970149253731343,
|
96 |
+
"eval_runtime": 1.064,
|
97 |
+
"eval_samples_per_second": 251.884,
|
98 |
+
"eval_steps_per_second": 4.699,
|
99 |
+
"step": 295
|
100 |
+
},
|
101 |
+
{
|
102 |
+
"epoch": 6.0,
|
103 |
+
"learning_rate": 9.912926619919478e-06,
|
104 |
+
"loss": 0.1819,
|
105 |
+
"step": 354
|
106 |
+
},
|
107 |
+
{
|
108 |
+
"epoch": 6.0,
|
109 |
+
"eval_accuracy": 0.8731343283582089,
|
110 |
+
"eval_f1": 0.706896551724138,
|
111 |
+
"eval_loss": 0.42611974477767944,
|
112 |
+
"eval_precision": 0.8367346938775511,
|
113 |
+
"eval_recall": 0.6119402985074627,
|
114 |
+
"eval_runtime": 1.0705,
|
115 |
+
"eval_samples_per_second": 250.352,
|
116 |
+
"eval_steps_per_second": 4.671,
|
117 |
+
"step": 354
|
118 |
+
},
|
119 |
+
{
|
120 |
+
"epoch": 7.0,
|
121 |
+
"learning_rate": 9.881319915089625e-06,
|
122 |
+
"loss": 0.148,
|
123 |
+
"step": 413
|
124 |
+
},
|
125 |
+
{
|
126 |
+
"epoch": 7.0,
|
127 |
+
"eval_accuracy": 0.8544776119402985,
|
128 |
+
"eval_f1": 0.6776859504132231,
|
129 |
+
"eval_loss": 0.5371143817901611,
|
130 |
+
"eval_precision": 0.7592592592592593,
|
131 |
+
"eval_recall": 0.6119402985074627,
|
132 |
+
"eval_runtime": 1.0641,
|
133 |
+
"eval_samples_per_second": 251.866,
|
134 |
+
"eval_steps_per_second": 4.699,
|
135 |
+
"step": 413
|
136 |
+
},
|
137 |
+
{
|
138 |
+
"epoch": 8.0,
|
139 |
+
"learning_rate": 9.844895936832474e-06,
|
140 |
+
"loss": 0.0995,
|
141 |
+
"step": 472
|
142 |
+
},
|
143 |
+
{
|
144 |
+
"epoch": 8.0,
|
145 |
+
"eval_accuracy": 0.8395522388059702,
|
146 |
+
"eval_f1": 0.6766917293233083,
|
147 |
+
"eval_loss": 0.6810328364372253,
|
148 |
+
"eval_precision": 0.6818181818181818,
|
149 |
+
"eval_recall": 0.6716417910447762,
|
150 |
+
"eval_runtime": 1.0652,
|
151 |
+
"eval_samples_per_second": 251.603,
|
152 |
+
"eval_steps_per_second": 4.694,
|
153 |
+
"step": 472
|
154 |
+
},
|
155 |
+
{
|
156 |
+
"epoch": 9.0,
|
157 |
+
"learning_rate": 9.803690631217043e-06,
|
158 |
+
"loss": 0.0843,
|
159 |
+
"step": 531
|
160 |
+
},
|
161 |
+
{
|
162 |
+
"epoch": 9.0,
|
163 |
+
"eval_accuracy": 0.8208955223880597,
|
164 |
+
"eval_f1": 0.5384615384615384,
|
165 |
+
"eval_loss": 0.8350497484207153,
|
166 |
+
"eval_precision": 0.7567567567567568,
|
167 |
+
"eval_recall": 0.417910447761194,
|
168 |
+
"eval_runtime": 1.0631,
|
169 |
+
"eval_samples_per_second": 252.096,
|
170 |
+
"eval_steps_per_second": 4.703,
|
171 |
+
"step": 531
|
172 |
+
},
|
173 |
+
{
|
174 |
+
"epoch": 9.0,
|
175 |
+
"step": 531,
|
176 |
+
"total_flos": 1111775764423680.0,
|
177 |
+
"train_loss": 0.2750625179312323,
|
178 |
+
"train_runtime": 261.0331,
|
179 |
+
"train_samples_per_second": 719.449,
|
180 |
+
"train_steps_per_second": 22.602
|
181 |
+
}
|
182 |
+
],
|
183 |
+
"logging_steps": 500,
|
184 |
+
"max_steps": 5900,
|
185 |
+
"num_input_tokens_seen": 0,
|
186 |
+
"num_train_epochs": 100,
|
187 |
+
"save_steps": 500,
|
188 |
+
"total_flos": 1111775764423680.0,
|
189 |
+
"train_batch_size": 32,
|
190 |
+
"trial_name": null,
|
191 |
+
"trial_params": null
|
192 |
+
}
|