Initial Commit
Browse files- README.md +111 -111
- eval_result_ner.json +1 -1
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
CHANGED
|
@@ -1,14 +1,14 @@
|
|
| 1 |
---
|
| 2 |
-
base_model: FacebookAI/xlm-roberta-base
|
| 3 |
library_name: transformers
|
| 4 |
license: mit
|
|
|
|
|
|
|
|
|
|
| 5 |
metrics:
|
| 6 |
- precision
|
| 7 |
- recall
|
| 8 |
- f1
|
| 9 |
- accuracy
|
| 10 |
-
tags:
|
| 11 |
-
- generated_from_trainer
|
| 12 |
model-index:
|
| 13 |
- name: scenario-non-kd-scr-ner-half-xlmr_data-univner_full44
|
| 14 |
results: []
|
|
@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 21 |
|
| 22 |
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
|
| 23 |
It achieves the following results on the evaluation set:
|
| 24 |
-
- Loss: 0.
|
| 25 |
-
- Precision: 0.
|
| 26 |
-
- Recall: 0.
|
| 27 |
-
- F1: 0.
|
| 28 |
-
- Accuracy: 0.
|
| 29 |
|
| 30 |
## Model description
|
| 31 |
|
|
@@ -56,109 +56,109 @@ The following hyperparameters were used during training:
|
|
| 56 |
|
| 57 |
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
| 58 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
| 59 |
-
| 0.
|
| 60 |
-
| 0.
|
| 61 |
-
| 0.
|
| 62 |
-
| 0.
|
| 63 |
-
| 0.
|
| 64 |
-
| 0.
|
| 65 |
-
| 0.
|
| 66 |
-
| 0.
|
| 67 |
-
| 0.
|
| 68 |
-
| 0.
|
| 69 |
-
| 0.
|
| 70 |
-
| 0.
|
| 71 |
-
| 0.
|
| 72 |
-
| 0.
|
| 73 |
-
| 0.
|
| 74 |
-
| 0.
|
| 75 |
-
| 0.
|
| 76 |
-
| 0.
|
| 77 |
-
| 0.
|
| 78 |
-
| 0.
|
| 79 |
-
| 0.
|
| 80 |
-
| 0.
|
| 81 |
-
| 0.
|
| 82 |
-
| 0.
|
| 83 |
-
| 0.0714 | 7.2759 | 12500 | 0.
|
| 84 |
-
| 0.
|
| 85 |
-
| 0.
|
| 86 |
-
| 0.
|
| 87 |
-
| 0.
|
| 88 |
-
| 0.
|
| 89 |
-
| 0.
|
| 90 |
-
| 0.
|
| 91 |
-
| 0.
|
| 92 |
-
| 0.
|
| 93 |
-
| 0.
|
| 94 |
-
| 0.
|
| 95 |
-
| 0.
|
| 96 |
-
| 0.
|
| 97 |
-
| 0.
|
| 98 |
-
| 0.
|
| 99 |
-
| 0.
|
| 100 |
-
| 0.
|
| 101 |
-
| 0.
|
| 102 |
-
| 0.
|
| 103 |
-
| 0.
|
| 104 |
-
| 0.
|
| 105 |
-
| 0.
|
| 106 |
-
| 0.
|
| 107 |
-
| 0.
|
| 108 |
-
| 0.
|
| 109 |
-
| 0.
|
| 110 |
-
| 0.
|
| 111 |
-
| 0.
|
| 112 |
-
| 0.
|
| 113 |
-
| 0.
|
| 114 |
-
| 0.
|
| 115 |
-
| 0.
|
| 116 |
-
| 0.
|
| 117 |
-
| 0.
|
| 118 |
-
| 0.
|
| 119 |
-
| 0.
|
| 120 |
-
| 0.
|
| 121 |
-
| 0.0119 | 18.3353 | 31500 | 0.
|
| 122 |
-
| 0.
|
| 123 |
-
| 0.
|
| 124 |
-
| 0.
|
| 125 |
-
| 0.
|
| 126 |
-
| 0.
|
| 127 |
-
| 0.
|
| 128 |
-
| 0.
|
| 129 |
-
| 0.
|
| 130 |
-
| 0.
|
| 131 |
-
| 0.
|
| 132 |
-
| 0.
|
| 133 |
-
| 0.
|
| 134 |
-
| 0.
|
| 135 |
-
| 0.
|
| 136 |
-
| 0.
|
| 137 |
-
| 0.
|
| 138 |
-
| 0.
|
| 139 |
-
| 0.0062 | 23.5739 | 40500 | 0.
|
| 140 |
-
| 0.
|
| 141 |
-
| 0.
|
| 142 |
-
| 0.
|
| 143 |
-
| 0.
|
| 144 |
-
| 0.
|
| 145 |
-
| 0.
|
| 146 |
-
| 0.
|
| 147 |
-
| 0.
|
| 148 |
-
| 0.
|
| 149 |
-
| 0.
|
| 150 |
-
| 0.
|
| 151 |
-
| 0.
|
| 152 |
-
| 0.
|
| 153 |
-
| 0.
|
| 154 |
-
| 0.
|
| 155 |
-
| 0.
|
| 156 |
-
| 0.
|
| 157 |
-
| 0.
|
| 158 |
-
| 0.
|
| 159 |
-
| 0.
|
| 160 |
-
| 0.
|
| 161 |
-
| 0.
|
| 162 |
|
| 163 |
|
| 164 |
### Framework versions
|
|
|
|
| 1 |
---
|
|
|
|
| 2 |
library_name: transformers
|
| 3 |
license: mit
|
| 4 |
+
base_model: FacebookAI/xlm-roberta-base
|
| 5 |
+
tags:
|
| 6 |
+
- generated_from_trainer
|
| 7 |
metrics:
|
| 8 |
- precision
|
| 9 |
- recall
|
| 10 |
- f1
|
| 11 |
- accuracy
|
|
|
|
|
|
|
| 12 |
model-index:
|
| 13 |
- name: scenario-non-kd-scr-ner-half-xlmr_data-univner_full44
|
| 14 |
results: []
|
|
|
|
| 21 |
|
| 22 |
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
|
| 23 |
It achieves the following results on the evaluation set:
|
| 24 |
+
- Loss: 0.3603
|
| 25 |
+
- Precision: 0.5290
|
| 26 |
+
- Recall: 0.5350
|
| 27 |
+
- F1: 0.5320
|
| 28 |
+
- Accuracy: 0.9557
|
| 29 |
|
| 30 |
## Model description
|
| 31 |
|
|
|
|
| 56 |
|
| 57 |
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|
| 58 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
|
| 59 |
+
| 0.3576 | 0.2910 | 500 | 0.2944 | 0.4546 | 0.0903 | 0.1507 | 0.9285 |
|
| 60 |
+
| 0.2752 | 0.5821 | 1000 | 0.2516 | 0.2788 | 0.1799 | 0.2187 | 0.9317 |
|
| 61 |
+
| 0.2421 | 0.8731 | 1500 | 0.2433 | 0.3406 | 0.2018 | 0.2535 | 0.9349 |
|
| 62 |
+
| 0.2184 | 1.1641 | 2000 | 0.2271 | 0.3200 | 0.2548 | 0.2837 | 0.9368 |
|
| 63 |
+
| 0.2079 | 1.4552 | 2500 | 0.2195 | 0.3445 | 0.2835 | 0.3110 | 0.9378 |
|
| 64 |
+
| 0.1943 | 1.7462 | 3000 | 0.2126 | 0.3641 | 0.2946 | 0.3257 | 0.9400 |
|
| 65 |
+
| 0.183 | 2.0373 | 3500 | 0.2067 | 0.3467 | 0.3354 | 0.3410 | 0.9394 |
|
| 66 |
+
| 0.1609 | 2.3283 | 4000 | 0.2107 | 0.3788 | 0.3251 | 0.3499 | 0.9414 |
|
| 67 |
+
| 0.1644 | 2.6193 | 4500 | 0.2114 | 0.3478 | 0.3389 | 0.3433 | 0.9395 |
|
| 68 |
+
| 0.1586 | 2.9104 | 5000 | 0.2053 | 0.3758 | 0.3559 | 0.3656 | 0.9414 |
|
| 69 |
+
| 0.147 | 3.2014 | 5500 | 0.2029 | 0.4010 | 0.3751 | 0.3877 | 0.9430 |
|
| 70 |
+
| 0.1423 | 3.4924 | 6000 | 0.2043 | 0.4021 | 0.3747 | 0.3879 | 0.9437 |
|
| 71 |
+
| 0.1363 | 3.7835 | 6500 | 0.2080 | 0.4074 | 0.3496 | 0.3763 | 0.9436 |
|
| 72 |
+
| 0.133 | 4.0745 | 7000 | 0.2049 | 0.4037 | 0.3919 | 0.3977 | 0.9439 |
|
| 73 |
+
| 0.1227 | 4.3655 | 7500 | 0.2048 | 0.4163 | 0.3976 | 0.4068 | 0.9441 |
|
| 74 |
+
| 0.1214 | 4.6566 | 8000 | 0.2034 | 0.4082 | 0.3984 | 0.4032 | 0.9439 |
|
| 75 |
+
| 0.1198 | 4.9476 | 8500 | 0.1960 | 0.4205 | 0.4249 | 0.4227 | 0.9452 |
|
| 76 |
+
| 0.1058 | 5.2386 | 9000 | 0.2107 | 0.4228 | 0.4048 | 0.4137 | 0.9459 |
|
| 77 |
+
| 0.103 | 5.5297 | 9500 | 0.1999 | 0.4451 | 0.4268 | 0.4358 | 0.9461 |
|
| 78 |
+
| 0.1017 | 5.8207 | 10000 | 0.2005 | 0.4308 | 0.4313 | 0.4310 | 0.9468 |
|
| 79 |
+
| 0.0956 | 6.1118 | 10500 | 0.2008 | 0.4388 | 0.4535 | 0.4460 | 0.9473 |
|
| 80 |
+
| 0.0848 | 6.4028 | 11000 | 0.2039 | 0.4301 | 0.4711 | 0.4496 | 0.9467 |
|
| 81 |
+
| 0.0834 | 6.6938 | 11500 | 0.2040 | 0.4535 | 0.4669 | 0.4601 | 0.9490 |
|
| 82 |
+
| 0.08 | 6.9849 | 12000 | 0.2021 | 0.4517 | 0.4753 | 0.4632 | 0.9495 |
|
| 83 |
+
| 0.0714 | 7.2759 | 12500 | 0.2095 | 0.4691 | 0.4604 | 0.4647 | 0.9503 |
|
| 84 |
+
| 0.0691 | 7.5669 | 13000 | 0.2165 | 0.4749 | 0.4425 | 0.4581 | 0.9509 |
|
| 85 |
+
| 0.0676 | 7.8580 | 13500 | 0.2174 | 0.4795 | 0.4513 | 0.4650 | 0.9512 |
|
| 86 |
+
| 0.0608 | 8.1490 | 14000 | 0.2157 | 0.4833 | 0.4812 | 0.4822 | 0.9516 |
|
| 87 |
+
| 0.0574 | 8.4400 | 14500 | 0.2145 | 0.4707 | 0.4937 | 0.4819 | 0.9512 |
|
| 88 |
+
| 0.06 | 8.7311 | 15000 | 0.2190 | 0.4862 | 0.4969 | 0.4915 | 0.9514 |
|
| 89 |
+
| 0.0555 | 9.0221 | 15500 | 0.2291 | 0.4840 | 0.4679 | 0.4758 | 0.9519 |
|
| 90 |
+
| 0.0491 | 9.3132 | 16000 | 0.2285 | 0.4845 | 0.4888 | 0.4866 | 0.9519 |
|
| 91 |
+
| 0.0481 | 9.6042 | 16500 | 0.2325 | 0.4934 | 0.4799 | 0.4865 | 0.9523 |
|
| 92 |
+
| 0.0476 | 9.8952 | 17000 | 0.2297 | 0.4678 | 0.5099 | 0.4879 | 0.9516 |
|
| 93 |
+
| 0.042 | 10.1863 | 17500 | 0.2336 | 0.4962 | 0.5012 | 0.4987 | 0.9524 |
|
| 94 |
+
| 0.0399 | 10.4773 | 18000 | 0.2368 | 0.5054 | 0.5024 | 0.5039 | 0.9530 |
|
| 95 |
+
| 0.0432 | 10.7683 | 18500 | 0.2408 | 0.5064 | 0.4885 | 0.4973 | 0.9529 |
|
| 96 |
+
| 0.0371 | 11.0594 | 19000 | 0.2471 | 0.4870 | 0.5122 | 0.4993 | 0.9530 |
|
| 97 |
+
| 0.0345 | 11.3504 | 19500 | 0.2485 | 0.4988 | 0.5077 | 0.5032 | 0.9532 |
|
| 98 |
+
| 0.0345 | 11.6414 | 20000 | 0.2533 | 0.5035 | 0.4918 | 0.4976 | 0.9533 |
|
| 99 |
+
| 0.0342 | 11.9325 | 20500 | 0.2524 | 0.4906 | 0.5115 | 0.5008 | 0.9528 |
|
| 100 |
+
| 0.0301 | 12.2235 | 21000 | 0.2562 | 0.4987 | 0.5014 | 0.5000 | 0.9531 |
|
| 101 |
+
| 0.0293 | 12.5146 | 21500 | 0.2572 | 0.5046 | 0.5113 | 0.5080 | 0.9532 |
|
| 102 |
+
| 0.0282 | 12.8056 | 22000 | 0.2633 | 0.5034 | 0.4972 | 0.5003 | 0.9537 |
|
| 103 |
+
| 0.0283 | 13.0966 | 22500 | 0.2680 | 0.5014 | 0.5119 | 0.5066 | 0.9532 |
|
| 104 |
+
| 0.0257 | 13.3877 | 23000 | 0.2685 | 0.4985 | 0.5185 | 0.5083 | 0.9537 |
|
| 105 |
+
| 0.0245 | 13.6787 | 23500 | 0.2735 | 0.5104 | 0.5165 | 0.5134 | 0.9541 |
|
| 106 |
+
| 0.0243 | 13.9697 | 24000 | 0.2811 | 0.4987 | 0.5157 | 0.5070 | 0.9534 |
|
| 107 |
+
| 0.0215 | 14.2608 | 24500 | 0.2769 | 0.5021 | 0.5299 | 0.5156 | 0.9538 |
|
| 108 |
+
| 0.0207 | 14.5518 | 25000 | 0.2748 | 0.4976 | 0.5425 | 0.5191 | 0.9532 |
|
| 109 |
+
| 0.0224 | 14.8428 | 25500 | 0.2835 | 0.5190 | 0.5151 | 0.5170 | 0.9545 |
|
| 110 |
+
| 0.0204 | 15.1339 | 26000 | 0.2845 | 0.5022 | 0.5195 | 0.5107 | 0.9541 |
|
| 111 |
+
| 0.0186 | 15.4249 | 26500 | 0.2922 | 0.5177 | 0.5057 | 0.5116 | 0.9541 |
|
| 112 |
+
| 0.0185 | 15.7159 | 27000 | 0.2888 | 0.5236 | 0.5210 | 0.5223 | 0.9546 |
|
| 113 |
+
| 0.018 | 16.0070 | 27500 | 0.2892 | 0.5029 | 0.5354 | 0.5187 | 0.9540 |
|
| 114 |
+
| 0.0152 | 16.2980 | 28000 | 0.2992 | 0.5166 | 0.5219 | 0.5192 | 0.9547 |
|
| 115 |
+
| 0.0159 | 16.5891 | 28500 | 0.3011 | 0.5127 | 0.5232 | 0.5179 | 0.9545 |
|
| 116 |
+
| 0.0159 | 16.8801 | 29000 | 0.3051 | 0.5135 | 0.5172 | 0.5153 | 0.9545 |
|
| 117 |
+
| 0.015 | 17.1711 | 29500 | 0.3000 | 0.5170 | 0.5233 | 0.5201 | 0.9544 |
|
| 118 |
+
| 0.0144 | 17.4622 | 30000 | 0.3049 | 0.5045 | 0.5180 | 0.5111 | 0.9543 |
|
| 119 |
+
| 0.0144 | 17.7532 | 30500 | 0.3040 | 0.5066 | 0.5470 | 0.5260 | 0.9545 |
|
| 120 |
+
| 0.0131 | 18.0442 | 31000 | 0.3145 | 0.5144 | 0.5214 | 0.5179 | 0.9547 |
|
| 121 |
+
| 0.0119 | 18.3353 | 31500 | 0.3129 | 0.5150 | 0.5351 | 0.5249 | 0.9546 |
|
| 122 |
+
| 0.013 | 18.6263 | 32000 | 0.3156 | 0.5113 | 0.5308 | 0.5208 | 0.9544 |
|
| 123 |
+
| 0.0121 | 18.9173 | 32500 | 0.3242 | 0.5334 | 0.5129 | 0.5229 | 0.9551 |
|
| 124 |
+
| 0.0115 | 19.2084 | 33000 | 0.3194 | 0.5244 | 0.5327 | 0.5285 | 0.9554 |
|
| 125 |
+
| 0.0118 | 19.4994 | 33500 | 0.3195 | 0.5137 | 0.5396 | 0.5263 | 0.9546 |
|
| 126 |
+
| 0.0107 | 19.7905 | 34000 | 0.3209 | 0.5107 | 0.5416 | 0.5257 | 0.9544 |
|
| 127 |
+
| 0.0095 | 20.0815 | 34500 | 0.3266 | 0.5235 | 0.5361 | 0.5298 | 0.9550 |
|
| 128 |
+
| 0.01 | 20.3725 | 35000 | 0.3252 | 0.5167 | 0.5370 | 0.5267 | 0.9549 |
|
| 129 |
+
| 0.01 | 20.6636 | 35500 | 0.3272 | 0.5177 | 0.5354 | 0.5264 | 0.9549 |
|
| 130 |
+
| 0.0095 | 20.9546 | 36000 | 0.3290 | 0.5171 | 0.5328 | 0.5248 | 0.9550 |
|
| 131 |
+
| 0.0089 | 21.2456 | 36500 | 0.3316 | 0.5255 | 0.5348 | 0.5301 | 0.9556 |
|
| 132 |
+
| 0.0085 | 21.5367 | 37000 | 0.3361 | 0.5258 | 0.5333 | 0.5295 | 0.9553 |
|
| 133 |
+
| 0.0089 | 21.8277 | 37500 | 0.3336 | 0.5320 | 0.5227 | 0.5273 | 0.9553 |
|
| 134 |
+
| 0.009 | 22.1187 | 38000 | 0.3368 | 0.5287 | 0.5263 | 0.5275 | 0.9555 |
|
| 135 |
+
| 0.0069 | 22.4098 | 38500 | 0.3374 | 0.5194 | 0.5325 | 0.5259 | 0.9553 |
|
| 136 |
+
| 0.0082 | 22.7008 | 39000 | 0.3367 | 0.5161 | 0.5376 | 0.5266 | 0.9552 |
|
| 137 |
+
| 0.0079 | 22.9919 | 39500 | 0.3362 | 0.5170 | 0.5385 | 0.5275 | 0.9549 |
|
| 138 |
+
| 0.0075 | 23.2829 | 40000 | 0.3408 | 0.5204 | 0.5309 | 0.5256 | 0.9552 |
|
| 139 |
+
| 0.0062 | 23.5739 | 40500 | 0.3469 | 0.5194 | 0.5284 | 0.5239 | 0.9551 |
|
| 140 |
+
| 0.0074 | 23.8650 | 41000 | 0.3394 | 0.5206 | 0.5409 | 0.5306 | 0.9553 |
|
| 141 |
+
| 0.0065 | 24.1560 | 41500 | 0.3424 | 0.5163 | 0.5429 | 0.5293 | 0.9553 |
|
| 142 |
+
| 0.0061 | 24.4470 | 42000 | 0.3466 | 0.5263 | 0.5330 | 0.5296 | 0.9550 |
|
| 143 |
+
| 0.0065 | 24.7381 | 42500 | 0.3490 | 0.5297 | 0.5318 | 0.5308 | 0.9556 |
|
| 144 |
+
| 0.0065 | 25.0291 | 43000 | 0.3523 | 0.5241 | 0.5344 | 0.5292 | 0.9553 |
|
| 145 |
+
| 0.0056 | 25.3201 | 43500 | 0.3485 | 0.5287 | 0.5348 | 0.5317 | 0.9551 |
|
| 146 |
+
| 0.0061 | 25.6112 | 44000 | 0.3515 | 0.5187 | 0.5490 | 0.5334 | 0.9552 |
|
| 147 |
+
| 0.0059 | 25.9022 | 44500 | 0.3559 | 0.5254 | 0.5292 | 0.5273 | 0.9554 |
|
| 148 |
+
| 0.0052 | 26.1932 | 45000 | 0.3521 | 0.5292 | 0.5340 | 0.5316 | 0.9555 |
|
| 149 |
+
| 0.0051 | 26.4843 | 45500 | 0.3545 | 0.5222 | 0.5403 | 0.5311 | 0.9556 |
|
| 150 |
+
| 0.0057 | 26.7753 | 46000 | 0.3551 | 0.5249 | 0.5374 | 0.5311 | 0.9554 |
|
| 151 |
+
| 0.0055 | 27.0664 | 46500 | 0.3534 | 0.5198 | 0.5491 | 0.5341 | 0.9554 |
|
| 152 |
+
| 0.0047 | 27.3574 | 47000 | 0.3572 | 0.5245 | 0.5318 | 0.5281 | 0.9556 |
|
| 153 |
+
| 0.0054 | 27.6484 | 47500 | 0.3566 | 0.5290 | 0.5374 | 0.5332 | 0.9557 |
|
| 154 |
+
| 0.0045 | 27.9395 | 48000 | 0.3560 | 0.5269 | 0.5405 | 0.5336 | 0.9554 |
|
| 155 |
+
| 0.0053 | 28.2305 | 48500 | 0.3587 | 0.5244 | 0.5373 | 0.5308 | 0.9556 |
|
| 156 |
+
| 0.0048 | 28.5215 | 49000 | 0.3605 | 0.5268 | 0.5353 | 0.5310 | 0.9555 |
|
| 157 |
+
| 0.0043 | 28.8126 | 49500 | 0.3569 | 0.5243 | 0.5385 | 0.5313 | 0.9553 |
|
| 158 |
+
| 0.0049 | 29.1036 | 50000 | 0.3585 | 0.5251 | 0.5370 | 0.5310 | 0.9555 |
|
| 159 |
+
| 0.0045 | 29.3946 | 50500 | 0.3591 | 0.5277 | 0.5359 | 0.5317 | 0.9555 |
|
| 160 |
+
| 0.0046 | 29.6857 | 51000 | 0.3598 | 0.5260 | 0.5347 | 0.5303 | 0.9555 |
|
| 161 |
+
| 0.0045 | 29.9767 | 51500 | 0.3603 | 0.5290 | 0.5350 | 0.5320 | 0.9557 |
|
| 162 |
|
| 163 |
|
| 164 |
### Framework versions
|
eval_result_ner.json
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
{"ceb_gja": {"precision": 0.
|
|
|
|
| 1 |
+
{"ceb_gja": {"precision": 0.2543859649122807, "recall": 0.5918367346938775, "f1": 0.3558282208588957, "accuracy": 0.9196911196911197}, "en_pud": {"precision": 0.4077212806026365, "recall": 0.4027906976744186, "f1": 0.40524099204492275, "accuracy": 0.9446071023800529}, "de_pud": {"precision": 0.08227176220806794, "recall": 0.2983638113570741, "f1": 0.12897857291449968, "accuracy": 0.7782101167315175}, "pt_pud": {"precision": 0.4693708609271523, "recall": 0.5159235668789809, "f1": 0.49154746423927176, "accuracy": 0.9561242363395566}, "ru_pud": {"precision": 0.014287982219399904, "recall": 0.08687258687258688, "f1": 0.024539877300613498, "accuracy": 0.49031258072849393}, "sv_pud": {"precision": 0.4292866082603254, "recall": 0.3333333333333333, "f1": 0.3752735229759299, "accuracy": 0.9441706856783393}, "tl_trg": {"precision": 0.17647058823529413, "recall": 0.5217391304347826, "f1": 0.26373626373626374, "accuracy": 0.9073569482288828}, "tl_ugnayan": {"precision": 0.11, "recall": 0.3333333333333333, "f1": 0.16541353383458648, "accuracy": 0.8787602552415679}, "zh_gsd": {"precision": 0.4070904645476773, "recall": 0.43415906127770537, "f1": 0.4201892744479495, "accuracy": 0.9204961704961705}, "zh_gsdsimp": {"precision": 0.41836734693877553, "recall": 0.42988204456094364, "f1": 0.42404654169360056, "accuracy": 0.924908424908425}, "hr_set": {"precision": 0.6501628664495114, "recall": 0.7113328581610834, "f1": 0.6793737236215113, "accuracy": 0.9651690024732069}, "da_ddt": {"precision": 0.5087719298245614, "recall": 0.5190156599552572, "f1": 0.5138427464008859, "accuracy": 0.9654793973860122}, "en_ewt": {"precision": 0.4832881662149955, "recall": 0.49172794117647056, "f1": 0.4874715261958998, "accuracy": 0.9524644379806352}, "pt_bosque": {"precision": 0.48723559445660103, "recall": 0.5497942386831276, "f1": 0.5166279969064191, "accuracy": 0.9587740907114911}, "sr_set": {"precision": 0.7037861915367484, "recall": 0.7461629279811098, "f1": 0.7243553008595989, "accuracy": 0.9626127309342439}, "sk_snk": {"precision": 0.35142118863049093, "recall": 0.2972677595628415, "f1": 0.32208407341622264, "accuracy": 0.9163002512562815}, "sv_talbanken": {"precision": 0.5561224489795918, "recall": 0.5561224489795918, "f1": 0.5561224489795918, "accuracy": 0.9919026353241399}}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 427407404
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7a8ec7b4b1a2f91a769f096babeb92422978e6f660bf97e275e9b92e68e78940
|
| 3 |
size 427407404
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 5304
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b96b3c7bcb2846705cbcb0bfd9a8c6b505e5adb370116a77969c393bf8162fae
|
| 3 |
size 5304
|