Hugging Face's logo

machinelearningzuu
/
ptsd-summarization

Text2Text Generation
Transformers
TensorBoard
Safetensors
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Metrics Community
machinelearningzuu commited on
Commit
832ec16
·
verified ·
1 Parent(s): 2743c5f

Model save

Browse files
Files changed (2) hide show
  1. README.md +453 -0
  2. generation_config.json +7 -0
README.md ADDED
@@ -0,0 +1,453 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: transformers
3
+ license: apache-2.0
4
+ base_model: google-t5/t5-small
5
+ tags:
6
+ - generated_from_trainer
7
+ model-index:
8
+ - name: ptsd-summarization
9
+ results: []
10
+ ---
11
+
12
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
+ should probably proofread and complete it, then remove this comment. -->
14
+
15
+ # ptsd-summarization
16
+
17
+ This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset.
18
+ It achieves the following results on the evaluation set:
19
+ - Loss: 1.1631
20
+
21
+ ## Model description
22
+
23
+ More information needed
24
+
25
+ ## Intended uses & limitations
26
+
27
+ More information needed
28
+
29
+ ## Training and evaluation data
30
+
31
+ More information needed
32
+
33
+ ## Training procedure
34
+
35
+ ### Training hyperparameters
36
+
37
+ The following hyperparameters were used during training:
38
+ - learning_rate: 2e-05
39
+ - train_batch_size: 1
40
+ - eval_batch_size: 1
41
+ - seed: 42
42
+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
43
+ - lr_scheduler_type: linear
44
+ - num_epochs: 40
45
+ - mixed_precision_training: Native AMP
46
+
47
+ ### Training results
48
+
49
+ | Training Loss | Epoch | Step | Validation Loss |
50
+ |:-------------:|:-------:|:-----:|:---------------:|
51
+ | 1.9664 | 0.1012 | 200 | 1.6373 |
52
+ | 1.8479 | 0.2023 | 400 | 1.5448 |
53
+ | 1.7346 | 0.3035 | 600 | 1.4981 |
54
+ | 1.6646 | 0.4047 | 800 | 1.4699 |
55
+ | 1.7652 | 0.5058 | 1000 | 1.4416 |
56
+ | 1.6482 | 0.6070 | 1200 | 1.4232 |
57
+ | 1.6232 | 0.7081 | 1400 | 1.4059 |
58
+ | 1.6663 | 0.8093 | 1600 | 1.3927 |
59
+ | 1.6392 | 0.9105 | 1800 | 1.3796 |
60
+ | 1.5706 | 1.0116 | 2000 | 1.3685 |
61
+ | 1.5516 | 1.1128 | 2200 | 1.3630 |
62
+ | 1.4989 | 1.2140 | 2400 | 1.3545 |
63
+ | 1.5427 | 1.3151 | 2600 | 1.3473 |
64
+ | 1.5742 | 1.4163 | 2800 | 1.3387 |
65
+ | 1.4773 | 1.5175 | 3000 | 1.3315 |
66
+ | 1.5533 | 1.6186 | 3200 | 1.3202 |
67
+ | 1.522 | 1.7198 | 3400 | 1.3174 |
68
+ | 1.5131 | 1.8209 | 3600 | 1.3203 |
69
+ | 1.4758 | 1.9221 | 3800 | 1.3039 |
70
+ | 1.5195 | 2.0233 | 4000 | 1.2988 |
71
+ | 1.4134 | 2.1244 | 4200 | 1.3007 |
72
+ | 1.4578 | 2.2256 | 4400 | 1.2943 |
73
+ | 1.4839 | 2.3268 | 4600 | 1.2922 |
74
+ | 1.3859 | 2.4279 | 4800 | 1.2835 |
75
+ | 1.4397 | 2.5291 | 5000 | 1.2839 |
76
+ | 1.4392 | 2.6302 | 5200 | 1.2808 |
77
+ | 1.4348 | 2.7314 | 5400 | 1.2685 |
78
+ | 1.4376 | 2.8326 | 5600 | 1.2679 |
79
+ | 1.4433 | 2.9337 | 5800 | 1.2665 |
80
+ | 1.4712 | 3.0349 | 6000 | 1.2667 |
81
+ | 1.4009 | 3.1361 | 6200 | 1.2630 |
82
+ | 1.3355 | 3.2372 | 6400 | 1.2591 |
83
+ | 1.3932 | 3.3384 | 6600 | 1.2566 |
84
+ | 1.3751 | 3.4396 | 6800 | 1.2533 |
85
+ | 1.3201 | 3.5407 | 7000 | 1.2550 |
86
+ | 1.3698 | 3.6419 | 7200 | 1.2506 |
87
+ | 1.4137 | 3.7430 | 7400 | 1.2452 |
88
+ | 1.3821 | 3.8442 | 7600 | 1.2469 |
89
+ | 1.3258 | 3.9454 | 7800 | 1.2427 |
90
+ | 1.3382 | 4.0465 | 8000 | 1.2419 |
91
+ | 1.3297 | 4.1477 | 8200 | 1.2391 |
92
+ | 1.3575 | 4.2489 | 8400 | 1.2308 |
93
+ | 1.3013 | 4.3500 | 8600 | 1.2277 |
94
+ | 1.3181 | 4.4512 | 8800 | 1.2261 |
95
+ | 1.3194 | 4.5524 | 9000 | 1.2272 |
96
+ | 1.3027 | 4.6535 | 9200 | 1.2310 |
97
+ | 1.3017 | 4.7547 | 9400 | 1.2242 |
98
+ | 1.3105 | 4.8558 | 9600 | 1.2269 |
99
+ | 1.3222 | 4.9570 | 9800 | 1.2163 |
100
+ | 1.309 | 5.0582 | 10000 | 1.2171 |
101
+ | 1.2944 | 5.1593 | 10200 | 1.2078 |
102
+ | 1.2968 | 5.2605 | 10400 | 1.2102 |
103
+ | 1.2824 | 5.3617 | 10600 | 1.2115 |
104
+ | 1.2581 | 5.4628 | 10800 | 1.2173 |
105
+ | 1.2682 | 5.5640 | 11000 | 1.2124 |
106
+ | 1.2704 | 5.6651 | 11200 | 1.2126 |
107
+ | 1.2858 | 5.7663 | 11400 | 1.2036 |
108
+ | 1.2483 | 5.8675 | 11600 | 1.2090 |
109
+ | 1.3131 | 5.9686 | 11800 | 1.2041 |
110
+ | 1.2148 | 6.0698 | 12000 | 1.2006 |
111
+ | 1.2455 | 6.1710 | 12200 | 1.2046 |
112
+ | 1.2568 | 6.2721 | 12400 | 1.2053 |
113
+ | 1.2171 | 6.3733 | 12600 | 1.2013 |
114
+ | 1.2149 | 6.4745 | 12800 | 1.1979 |
115
+ | 1.2106 | 6.5756 | 13000 | 1.2003 |
116
+ | 1.244 | 6.6768 | 13200 | 1.1933 |
117
+ | 1.246 | 6.7779 | 13400 | 1.1920 |
118
+ | 1.2931 | 6.8791 | 13600 | 1.1925 |
119
+ | 1.1615 | 6.9803 | 13800 | 1.1913 |
120
+ | 1.2136 | 7.0814 | 14000 | 1.1914 |
121
+ | 1.1436 | 7.1826 | 14200 | 1.1939 |
122
+ | 1.2137 | 7.2838 | 14400 | 1.1911 |
123
+ | 1.1871 | 7.3849 | 14600 | 1.1886 |
124
+ | 1.2506 | 7.4861 | 14800 | 1.1831 |
125
+ | 1.1921 | 7.5873 | 15000 | 1.1833 |
126
+ | 1.2177 | 7.6884 | 15200 | 1.1799 |
127
+ | 1.1888 | 7.7896 | 15400 | 1.1817 |
128
+ | 1.1947 | 7.8907 | 15600 | 1.1792 |
129
+ | 1.1784 | 7.9919 | 15800 | 1.1797 |
130
+ | 1.2106 | 8.0931 | 16000 | 1.1794 |
131
+ | 1.2427 | 8.1942 | 16200 | 1.1768 |
132
+ | 1.1888 | 8.2954 | 16400 | 1.1817 |
133
+ | 1.1271 | 8.3966 | 16600 | 1.1811 |
134
+ | 1.1584 | 8.4977 | 16800 | 1.1743 |
135
+ | 1.1506 | 8.5989 | 17000 | 1.1764 |
136
+ | 1.146 | 8.7001 | 17200 | 1.1761 |
137
+ | 1.1299 | 8.8012 | 17400 | 1.1750 |
138
+ | 1.1653 | 8.9024 | 17600 | 1.1704 |
139
+ | 1.1112 | 9.0035 | 17800 | 1.1681 |
140
+ | 1.1845 | 9.1047 | 18000 | 1.1687 |
141
+ | 1.135 | 9.2059 | 18200 | 1.1721 |
142
+ | 1.111 | 9.3070 | 18400 | 1.1666 |
143
+ | 1.1325 | 9.4082 | 18600 | 1.1662 |
144
+ | 1.1284 | 9.5094 | 18800 | 1.1671 |
145
+ | 1.0956 | 9.6105 | 19000 | 1.1754 |
146
+ | 1.1614 | 9.7117 | 19200 | 1.1707 |
147
+ | 1.1138 | 9.8128 | 19400 | 1.1686 |
148
+ | 1.1287 | 9.9140 | 19600 | 1.1709 |
149
+ | 1.134 | 10.0152 | 19800 | 1.1694 |
150
+ | 1.1109 | 10.1163 | 20000 | 1.1700 |
151
+ | 1.1186 | 10.2175 | 20200 | 1.1684 |
152
+ | 1.1232 | 10.3187 | 20400 | 1.1698 |
153
+ | 1.1287 | 10.4198 | 20600 | 1.1702 |
154
+ | 1.1139 | 10.5210 | 20800 | 1.1718 |
155
+ | 1.1484 | 10.6222 | 21000 | 1.1710 |
156
+ | 1.072 | 10.7233 | 21200 | 1.1639 |
157
+ | 1.0957 | 10.8245 | 21400 | 1.1611 |
158
+ | 1.128 | 10.9256 | 21600 | 1.1640 |
159
+ | 1.0693 | 11.0268 | 21800 | 1.1590 |
160
+ | 1.0505 | 11.1280 | 22000 | 1.1619 |
161
+ | 1.0466 | 11.2291 | 22200 | 1.1654 |
162
+ | 1.079 | 11.3303 | 22400 | 1.1631 |
163
+ | 1.1081 | 11.4315 | 22600 | 1.1615 |
164
+ | 1.0918 | 11.5326 | 22800 | 1.1593 |
165
+ | 1.092 | 11.6338 | 23000 | 1.1579 |
166
+ | 1.1203 | 11.7350 | 23200 | 1.1609 |
167
+ | 1.0742 | 11.8361 | 23400 | 1.1551 |
168
+ | 1.067 | 11.9373 | 23600 | 1.1557 |
169
+ | 1.0514 | 12.0384 | 23800 | 1.1635 |
170
+ | 1.0464 | 12.1396 | 24000 | 1.1600 |
171
+ | 1.0569 | 12.2408 | 24200 | 1.1602 |
172
+ | 1.0431 | 12.3419 | 24400 | 1.1612 |
173
+ | 1.0772 | 12.4431 | 24600 | 1.1628 |
174
+ | 1.0517 | 12.5443 | 24800 | 1.1578 |
175
+ | 1.0648 | 12.6454 | 25000 | 1.1577 |
176
+ | 1.0513 | 12.7466 | 25200 | 1.1621 |
177
+ | 1.0594 | 12.8477 | 25400 | 1.1592 |
178
+ | 1.0768 | 12.9489 | 25600 | 1.1604 |
179
+ | 1.1028 | 13.0501 | 25800 | 1.1500 |
180
+ | 1.0269 | 13.1512 | 26000 | 1.1566 |
181
+ | 1.0388 | 13.2524 | 26200 | 1.1595 |
182
+ | 0.9966 | 13.3536 | 26400 | 1.1595 |
183
+ | 1.0287 | 13.4547 | 26600 | 1.1604 |
184
+ | 1.0528 | 13.5559 | 26800 | 1.1565 |
185
+ | 1.0425 | 13.6571 | 27000 | 1.1536 |
186
+ | 1.0547 | 13.7582 | 27200 | 1.1567 |
187
+ | 1.0125 | 13.8594 | 27400 | 1.1606 |
188
+ | 1.0743 | 13.9605 | 27600 | 1.1513 |
189
+ | 0.9734 | 14.0617 | 27800 | 1.1579 |
190
+ | 1.0261 | 14.1629 | 28000 | 1.1528 |
191
+ | 1.0001 | 14.2640 | 28200 | 1.1584 |
192
+ | 1.014 | 14.3652 | 28400 | 1.1596 |
193
+ | 1.0252 | 14.4664 | 28600 | 1.1622 |
194
+ | 1.0505 | 14.5675 | 28800 | 1.1538 |
195
+ | 1.0142 | 14.6687 | 29000 | 1.1567 |
196
+ | 1.0291 | 14.7699 | 29200 | 1.1515 |
197
+ | 1.0313 | 14.8710 | 29400 | 1.1550 |
198
+ | 0.9889 | 14.9722 | 29600 | 1.1554 |
199
+ | 1.0173 | 15.0733 | 29800 | 1.1588 |
200
+ | 1.0081 | 15.1745 | 30000 | 1.1587 |
201
+ | 0.9559 | 15.2757 | 30200 | 1.1539 |
202
+ | 1.0332 | 15.3768 | 30400 | 1.1573 |
203
+ | 1.0004 | 15.4780 | 30600 | 1.1553 |
204
+ | 0.9586 | 15.5792 | 30800 | 1.1496 |
205
+ | 1.0138 | 15.6803 | 31000 | 1.1513 |
206
+ | 1.0256 | 15.7815 | 31200 | 1.1545 |
207
+ | 1.0243 | 15.8827 | 31400 | 1.1546 |
208
+ | 1.0135 | 15.9838 | 31600 | 1.1540 |
209
+ | 0.9589 | 16.0850 | 31800 | 1.1524 |
210
+ | 0.9672 | 16.1861 | 32000 | 1.1574 |
211
+ | 1.0335 | 16.2873 | 32200 | 1.1539 |
212
+ | 0.9464 | 16.3885 | 32400 | 1.1637 |
213
+ | 0.9669 | 16.4896 | 32600 | 1.1579 |
214
+ | 0.9629 | 16.5908 | 32800 | 1.1611 |
215
+ | 0.9586 | 16.6920 | 33000 | 1.1575 |
216
+ | 0.9939 | 16.7931 | 33200 | 1.1582 |
217
+ | 0.9956 | 16.8943 | 33400 | 1.1544 |
218
+ | 1.0249 | 16.9954 | 33600 | 1.1520 |
219
+ | 0.9461 | 17.0966 | 33800 | 1.1541 |
220
+ | 0.9876 | 17.1978 | 34000 | 1.1520 |
221
+ | 0.9656 | 17.2989 | 34200 | 1.1530 |
222
+ | 0.9443 | 17.4001 | 34400 | 1.1565 |
223
+ | 0.9348 | 17.5013 | 34600 | 1.1570 |
224
+ | 0.9906 | 17.6024 | 34800 | 1.1532 |
225
+ | 0.9281 | 17.7036 | 35000 | 1.1553 |
226
+ | 0.9346 | 17.8048 | 35200 | 1.1542 |
227
+ | 1.0175 | 17.9059 | 35400 | 1.1506 |
228
+ | 0.9547 | 18.0071 | 35600 | 1.1544 |
229
+ | 0.9089 | 18.1082 | 35800 | 1.1571 |
230
+ | 0.9506 | 18.2094 | 36000 | 1.1559 |
231
+ | 0.9625 | 18.3106 | 36200 | 1.1532 |
232
+ | 0.9206 | 18.4117 | 36400 | 1.1532 |
233
+ | 0.8997 | 18.5129 | 36600 | 1.1530 |
234
+ | 0.9625 | 18.6141 | 36800 | 1.1553 |
235
+ | 0.9358 | 18.7152 | 37000 | 1.1516 |
236
+ | 1.0168 | 18.8164 | 37200 | 1.1531 |
237
+ | 0.965 | 18.9176 | 37400 | 1.1489 |
238
+ | 0.9527 | 19.0187 | 37600 | 1.1463 |
239
+ | 0.9437 | 19.1199 | 37800 | 1.1511 |
240
+ | 0.916 | 19.2210 | 38000 | 1.1523 |
241
+ | 0.9264 | 19.3222 | 38200 | 1.1521 |
242
+ | 0.957 | 19.4234 | 38400 | 1.1507 |
243
+ | 0.9539 | 19.5245 | 38600 | 1.1533 |
244
+ | 0.9256 | 19.6257 | 38800 | 1.1498 |
245
+ | 0.908 | 19.7269 | 39000 | 1.1497 |
246
+ | 0.8984 | 19.8280 | 39200 | 1.1525 |
247
+ | 0.9754 | 19.9292 | 39400 | 1.1479 |
248
+ | 0.9266 | 20.0303 | 39600 | 1.1521 |
249
+ | 0.942 | 20.1315 | 39800 | 1.1513 |
250
+ | 0.9249 | 20.2327 | 40000 | 1.1485 |
251
+ | 0.8982 | 20.3338 | 40200 | 1.1505 |
252
+ | 0.8932 | 20.4350 | 40400 | 1.1541 |
253
+ | 0.926 | 20.5362 | 40600 | 1.1554 |
254
+ | 0.9393 | 20.6373 | 40800 | 1.1538 |
255
+ | 0.9498 | 20.7385 | 41000 | 1.1513 |
256
+ | 0.9262 | 20.8397 | 41200 | 1.1546 |
257
+ | 0.9085 | 20.9408 | 41400 | 1.1519 |
258
+ | 0.924 | 21.0420 | 41600 | 1.1526 |
259
+ | 0.8869 | 21.1431 | 41800 | 1.1568 |
260
+ | 0.9009 | 21.2443 | 42000 | 1.1549 |
261
+ | 0.9097 | 21.3455 | 42200 | 1.1505 |
262
+ | 0.9179 | 21.4466 | 42400 | 1.1495 |
263
+ | 0.9519 | 21.5478 | 42600 | 1.1492 |
264
+ | 0.9473 | 21.6490 | 42800 | 1.1480 |
265
+ | 0.9488 | 21.7501 | 43000 | 1.1521 |
266
+ | 0.8719 | 21.8513 | 43200 | 1.1501 |
267
+ | 0.8593 | 21.9525 | 43400 | 1.1544 |
268
+ | 0.9029 | 22.0536 | 43600 | 1.1572 |
269
+ | 0.9005 | 22.1548 | 43800 | 1.1532 |
270
+ | 0.919 | 22.2559 | 44000 | 1.1521 |
271
+ | 0.9448 | 22.3571 | 44200 | 1.1506 |
272
+ | 0.9157 | 22.4583 | 44400 | 1.1575 |
273
+ | 0.8985 | 22.5594 | 44600 | 1.1528 |
274
+ | 0.8632 | 22.6606 | 44800 | 1.1571 |
275
+ | 0.8519 | 22.7618 | 45000 | 1.1521 |
276
+ | 0.8813 | 22.8629 | 45200 | 1.1543 |
277
+ | 0.9135 | 22.9641 | 45400 | 1.1524 |
278
+ | 0.9258 | 23.0653 | 45600 | 1.1533 |
279
+ | 0.8879 | 23.1664 | 45800 | 1.1509 |
280
+ | 0.8898 | 23.2676 | 46000 | 1.1557 |
281
+ | 0.8521 | 23.3687 | 46200 | 1.1578 |
282
+ | 0.8519 | 23.4699 | 46400 | 1.1574 |
283
+ | 0.9005 | 23.5711 | 46600 | 1.1552 |
284
+ | 0.8927 | 23.6722 | 46800 | 1.1534 |
285
+ | 0.8729 | 23.7734 | 47000 | 1.1565 |
286
+ | 0.9021 | 23.8746 | 47200 | 1.1533 |
287
+ | 0.9276 | 23.9757 | 47400 | 1.1560 |
288
+ | 0.8924 | 24.0769 | 47600 | 1.1545 |
289
+ | 0.8545 | 24.1780 | 47800 | 1.1525 |
290
+ | 0.8724 | 24.2792 | 48000 | 1.1501 |
291
+ | 0.9042 | 24.3804 | 48200 | 1.1554 |
292
+ | 0.9064 | 24.4815 | 48400 | 1.1523 |
293
+ | 0.8832 | 24.5827 | 48600 | 1.1512 |
294
+ | 0.8979 | 24.6839 | 48800 | 1.1532 |
295
+ | 0.8324 | 24.7850 | 49000 | 1.1528 |
296
+ | 0.8757 | 24.8862 | 49200 | 1.1550 |
297
+ | 0.8614 | 24.9874 | 49400 | 1.1545 |
298
+ | 0.8691 | 25.0885 | 49600 | 1.1545 |
299
+ | 0.8828 | 25.1897 | 49800 | 1.1557 |
300
+ | 0.8455 | 25.2908 | 50000 | 1.1565 |
301
+ | 0.8352 | 25.3920 | 50200 | 1.1526 |
302
+ | 0.8775 | 25.4932 | 50400 | 1.1527 |
303
+ | 0.832 | 25.5943 | 50600 | 1.1562 |
304
+ | 0.9212 | 25.6955 | 50800 | 1.1560 |
305
+ | 0.8921 | 25.7967 | 51000 | 1.1520 |
306
+ | 0.8611 | 25.8978 | 51200 | 1.1531 |
307
+ | 0.8633 | 25.9990 | 51400 | 1.1522 |
308
+ | 0.8508 | 26.1002 | 51600 | 1.1541 |
309
+ | 0.823 | 26.2013 | 51800 | 1.1550 |
310
+ | 0.9097 | 26.3025 | 52000 | 1.1558 |
311
+ | 0.8726 | 26.4036 | 52200 | 1.1537 |
312
+ | 0.8891 | 26.5048 | 52400 | 1.1545 |
313
+ | 0.8276 | 26.6060 | 52600 | 1.1559 |
314
+ | 0.8404 | 26.7071 | 52800 | 1.1543 |
315
+ | 0.8556 | 26.8083 | 53000 | 1.1510 |
316
+ | 0.8846 | 26.9095 | 53200 | 1.1544 |
317
+ | 0.8461 | 27.0106 | 53400 | 1.1533 |
318
+ | 0.8265 | 27.1118 | 53600 | 1.1516 |
319
+ | 0.8807 | 27.2129 | 53800 | 1.1529 |
320
+ | 0.8459 | 27.3141 | 54000 | 1.1554 |
321
+ | 0.8648 | 27.4153 | 54200 | 1.1556 |
322
+ | 0.8559 | 27.5164 | 54400 | 1.1592 |
323
+ | 0.8797 | 27.6176 | 54600 | 1.1546 |
324
+ | 0.8502 | 27.7188 | 54800 | 1.1565 |
325
+ | 0.83 | 27.8199 | 55000 | 1.1568 |
326
+ | 0.863 | 27.9211 | 55200 | 1.1582 |
327
+ | 0.8294 | 28.0223 | 55400 | 1.1564 |
328
+ | 0.8217 | 28.1234 | 55600 | 1.1565 |
329
+ | 0.8321 | 28.2246 | 55800 | 1.1568 |
330
+ | 0.8231 | 28.3257 | 56000 | 1.1559 |
331
+ | 0.8355 | 28.4269 | 56200 | 1.1539 |
332
+ | 0.8347 | 28.5281 | 56400 | 1.1546 |
333
+ | 0.8538 | 28.6292 | 56600 | 1.1555 |
334
+ | 0.8558 | 28.7304 | 56800 | 1.1572 |
335
+ | 0.8243 | 28.8316 | 57000 | 1.1576 |
336
+ | 0.9286 | 28.9327 | 57200 | 1.1575 |
337
+ | 0.8884 | 29.0339 | 57400 | 1.1558 |
338
+ | 0.8399 | 29.1351 | 57600 | 1.1578 |
339
+ | 0.8306 | 29.2362 | 57800 | 1.1567 |
340
+ | 0.8208 | 29.3374 | 58000 | 1.1566 |
341
+ | 0.8072 | 29.4385 | 58200 | 1.1585 |
342
+ | 0.8278 | 29.5397 | 58400 | 1.1591 |
343
+ | 0.8489 | 29.6409 | 58600 | 1.1579 |
344
+ | 0.8756 | 29.7420 | 58800 | 1.1542 |
345
+ | 0.8384 | 29.8432 | 59000 | 1.1560 |
346
+ | 0.8134 | 29.9444 | 59200 | 1.1584 |
347
+ | 0.8215 | 30.0455 | 59400 | 1.1604 |
348
+ | 0.8562 | 30.1467 | 59600 | 1.1584 |
349
+ | 0.8449 | 30.2479 | 59800 | 1.1612 |
350
+ | 0.8796 | 30.3490 | 60000 | 1.1585 |
351
+ | 0.9045 | 30.4502 | 60200 | 1.1548 |
352
+ | 0.7957 | 30.5513 | 60400 | 1.1574 |
353
+ | 0.7785 | 30.6525 | 60600 | 1.1571 |
354
+ | 0.8368 | 30.7537 | 60800 | 1.1592 |
355
+ | 0.8423 | 30.8548 | 61000 | 1.1588 |
356
+ | 0.7635 | 30.9560 | 61200 | 1.1597 |
357
+ | 0.8168 | 31.0572 | 61400 | 1.1588 |
358
+ | 0.8612 | 31.1583 | 61600 | 1.1590 |
359
+ | 0.83 | 31.2595 | 61800 | 1.1591 |
360
+ | 0.8217 | 31.3606 | 62000 | 1.1577 |
361
+ | 0.8413 | 31.4618 | 62200 | 1.1560 |
362
+ | 0.8382 | 31.5630 | 62400 | 1.1579 |
363
+ | 0.804 | 31.6641 | 62600 | 1.1590 |
364
+ | 0.8481 | 31.7653 | 62800 | 1.1586 |
365
+ | 0.8373 | 31.8665 | 63000 | 1.1577 |
366
+ | 0.778 | 31.9676 | 63200 | 1.1590 |
367
+ | 0.8349 | 32.0688 | 63400 | 1.1605 |
368
+ | 0.7616 | 32.1700 | 63600 | 1.1588 |
369
+ | 0.8354 | 32.2711 | 63800 | 1.1600 |
370
+ | 0.8107 | 32.3723 | 64000 | 1.1595 |
371
+ | 0.8092 | 32.4734 | 64200 | 1.1605 |
372
+ | 0.808 | 32.5746 | 64400 | 1.1596 |
373
+ | 0.8734 | 32.6758 | 64600 | 1.1586 |
374
+ | 0.806 | 32.7769 | 64800 | 1.1589 |
375
+ | 0.7934 | 32.8781 | 65000 | 1.1579 |
376
+ | 0.924 | 32.9793 | 65200 | 1.1592 |
377
+ | 0.8016 | 33.0804 | 65400 | 1.1600 |
378
+ | 0.8136 | 33.1816 | 65600 | 1.1608 |
379
+ | 0.8735 | 33.2828 | 65800 | 1.1603 |
380
+ | 0.8068 | 33.3839 | 66000 | 1.1602 |
381
+ | 0.8051 | 33.4851 | 66200 | 1.1618 |
382
+ | 0.8049 | 33.5862 | 66400 | 1.1623 |
383
+ | 0.8062 | 33.6874 | 66600 | 1.1621 |
384
+ | 0.7888 | 33.7886 | 66800 | 1.1636 |
385
+ | 0.8115 | 33.8897 | 67000 | 1.1653 |
386
+ | 0.8149 | 33.9909 | 67200 | 1.1639 |
387
+ | 0.7969 | 34.0921 | 67400 | 1.1621 |
388
+ | 0.8319 | 34.1932 | 67600 | 1.1621 |
389
+ | 0.8 | 34.2944 | 67800 | 1.1613 |
390
+ | 0.8332 | 34.3955 | 68000 | 1.1610 |
391
+ | 0.802 | 34.4967 | 68200 | 1.1601 |
392
+ | 0.8242 | 34.5979 | 68400 | 1.1618 |
393
+ | 0.7646 | 34.6990 | 68600 | 1.1634 |
394
+ | 0.7749 | 34.8002 | 68800 | 1.1634 |
395
+ | 0.8498 | 34.9014 | 69000 | 1.1619 |
396
+ | 0.8597 | 35.0025 | 69200 | 1.1619 |
397
+ | 0.8015 | 35.1037 | 69400 | 1.1607 |
398
+ | 0.8733 | 35.2049 | 69600 | 1.1604 |
399
+ | 0.8199 | 35.3060 | 69800 | 1.1621 |
400
+ | 0.7654 | 35.4072 | 70000 | 1.1626 |
401
+ | 0.7932 | 35.5083 | 70200 | 1.1628 |
402
+ | 0.8133 | 35.6095 | 70400 | 1.1611 |
403
+ | 0.7802 | 35.7107 | 70600 | 1.1612 |
404
+ | 0.8061 | 35.8118 | 70800 | 1.1604 |
405
+ | 0.7915 | 35.9130 | 71000 | 1.1610 |
406
+ | 0.8092 | 36.0142 | 71200 | 1.1619 |
407
+ | 0.7976 | 36.1153 | 71400 | 1.1627 |
408
+ | 0.8017 | 36.2165 | 71600 | 1.1627 |
409
+ | 0.79 | 36.3177 | 71800 | 1.1628 |
410
+ | 0.7558 | 36.4188 | 72000 | 1.1627 |
411
+ | 0.7938 | 36.5200 | 72200 | 1.1628 |
412
+ | 0.8048 | 36.6211 | 72400 | 1.1624 |
413
+ | 0.8593 | 36.7223 | 72600 | 1.1630 |
414
+ | 0.8314 | 36.8235 | 72800 | 1.1629 |
415
+ | 0.8283 | 36.9246 | 73000 | 1.1614 |
416
+ | 0.8174 | 37.0258 | 73200 | 1.1609 |
417
+ | 0.7722 | 37.1270 | 73400 | 1.1608 |
418
+ | 0.749 | 37.2281 | 73600 | 1.1611 |
419
+ | 0.7754 | 37.3293 | 73800 | 1.1607 |
420
+ | 0.781 | 37.4305 | 74000 | 1.1617 |
421
+ | 0.8335 | 37.5316 | 74200 | 1.1623 |
422
+ | 0.8072 | 37.6328 | 74400 | 1.1633 |
423
+ | 0.8042 | 37.7339 | 74600 | 1.1630 |
424
+ | 0.8393 | 37.8351 | 74800 | 1.1633 |
425
+ | 0.829 | 37.9363 | 75000 | 1.1627 |
426
+ | 0.8514 | 38.0374 | 75200 | 1.1626 |
427
+ | 0.7967 | 38.1386 | 75400 | 1.1632 |
428
+ | 0.7447 | 38.2398 | 75600 | 1.1634 |
429
+ | 0.78 | 38.3409 | 75800 | 1.1641 |
430
+ | 0.8281 | 38.4421 | 76000 | 1.1635 |
431
+ | 0.8021 | 38.5432 | 76200 | 1.1633 |
432
+ | 0.8328 | 38.6444 | 76400 | 1.1629 |
433
+ | 0.8464 | 38.7456 | 76600 | 1.1629 |
434
+ | 0.837 | 38.8467 | 76800 | 1.1625 |
435
+ | 0.7686 | 38.9479 | 77000 | 1.1627 |
436
+ | 0.8235 | 39.0491 | 77200 | 1.1625 |
437
+ | 0.8161 | 39.1502 | 77400 | 1.1626 |
438
+ | 0.8016 | 39.2514 | 77600 | 1.1627 |
439
+ | 0.7946 | 39.3526 | 77800 | 1.1630 |
440
+ | 0.7941 | 39.4537 | 78000 | 1.1633 |
441
+ | 0.76 | 39.5549 | 78200 | 1.1632 |
442
+ | 0.8394 | 39.6560 | 78400 | 1.1632 |
443
+ | 0.7558 | 39.7572 | 78600 | 1.1632 |
444
+ | 0.8374 | 39.8584 | 78800 | 1.1631 |
445
+ | 0.8077 | 39.9595 | 79000 | 1.1631 |
446
+
447
+
448
+ ### Framework versions
449
+
450
+ - Transformers 4.48.0
451
+ - Pytorch 2.5.1+cu124
452
+ - Datasets 3.3.0
453
+ - Tokenizers 0.21.0
generation_config.json ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "decoder_start_token_id": 0,
4
+ "eos_token_id": 1,
5
+ "pad_token_id": 0,
6
+ "transformers_version": "4.48.0"
7
+ }