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End of training

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  1. README.md +41 -55
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9346330275229358
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the glue dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2931
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- - Accuracy: 0.9346
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  ## Model description
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@@ -65,58 +65,44 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.6552 | 0.02 | 50 | 0.6446 | 0.6193 |
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- | 0.3237 | 0.05 | 100 | 0.2756 | 0.9071 |
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- | 0.2725 | 0.07 | 150 | 0.2409 | 0.9151 |
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- | 0.2353 | 0.1 | 200 | 0.2526 | 0.9128 |
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- | 0.2342 | 0.12 | 250 | 0.2287 | 0.9174 |
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- | 0.2635 | 0.14 | 300 | 0.2342 | 0.9220 |
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- | 0.2534 | 0.17 | 350 | 0.2149 | 0.9255 |
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- | 0.2402 | 0.19 | 400 | 0.2160 | 0.9255 |
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- | 0.1857 | 0.21 | 450 | 0.2117 | 0.9243 |
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- | 0.1696 | 0.24 | 500 | 0.3351 | 0.9266 |
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- | 0.1504 | 0.26 | 550 | 0.2275 | 0.9209 |
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- | 0.2849 | 0.29 | 600 | 0.2301 | 0.9255 |
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- | 0.2336 | 0.31 | 650 | 0.2332 | 0.9220 |
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- | 0.1587 | 0.33 | 700 | 0.2158 | 0.9243 |
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- | 0.2645 | 0.36 | 750 | 0.2075 | 0.9300 |
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- | 0.1809 | 0.38 | 800 | 0.2060 | 0.9255 |
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- | 0.1088 | 0.4 | 850 | 0.3409 | 0.9255 |
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- | 0.1623 | 0.43 | 900 | 0.3342 | 0.9289 |
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- | 0.1987 | 0.45 | 950 | 0.2280 | 0.9278 |
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- | 0.2622 | 0.48 | 1000 | 0.3327 | 0.9243 |
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- | 0.1121 | 0.5 | 1050 | 0.3205 | 0.9289 |
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- | 0.1831 | 0.52 | 1100 | 0.4233 | 0.9243 |
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- | 0.2456 | 0.55 | 1150 | 0.5359 | 0.9335 |
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- | 0.0938 | 0.57 | 1200 | 0.1931 | 0.9358 |
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- | 0.1321 | 0.59 | 1250 | 0.4359 | 0.9323 |
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- | 0.1478 | 0.62 | 1300 | 0.3059 | 0.9346 |
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- | 0.1819 | 0.64 | 1350 | 0.4172 | 0.9358 |
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- | 0.1178 | 0.67 | 1400 | 0.2997 | 0.9358 |
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- | 0.1426 | 0.69 | 1450 | 0.5336 | 0.9346 |
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- | 0.1033 | 0.71 | 1500 | 0.4292 | 0.9300 |
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- | 0.1357 | 0.74 | 1550 | 0.4310 | 0.9369 |
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- | 0.1668 | 0.76 | 1600 | 0.5359 | 0.9358 |
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- | 0.1438 | 0.78 | 1650 | 0.3025 | 0.9381 |
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- | 0.2141 | 0.81 | 1700 | 0.4265 | 0.9323 |
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- | 0.0899 | 0.83 | 1750 | 0.4217 | 0.9323 |
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- | 0.1062 | 0.86 | 1800 | 0.4377 | 0.9289 |
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- | 0.1557 | 0.88 | 1850 | 0.3003 | 0.9323 |
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- | 0.1237 | 0.9 | 1900 | 0.3134 | 0.9358 |
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- | 0.1172 | 0.93 | 1950 | 0.3199 | 0.9312 |
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- | 0.1617 | 0.95 | 2000 | 0.2931 | 0.9346 |
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- | 0.1293 | 0.97 | 2050 | 0.2978 | 0.9381 |
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- | 0.1686 | 1.0 | 2100 | 0.2885 | 0.9369 |
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- | 0.7247 | 1.02 | 2150 | 0.7872 | 0.9300 |
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- | 0.0679 | 1.05 | 2200 | 0.3114 | 0.9404 |
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- | 0.0522 | 1.07 | 2250 | 0.2998 | 0.9346 |
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- | 0.078 | 1.09 | 2300 | 0.3418 | 0.9358 |
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- | 0.0749 | 1.12 | 2350 | 0.3248 | 0.9381 |
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- | 0.0483 | 1.14 | 2400 | 0.4340 | 0.9369 |
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- | 0.1534 | 1.16 | 2450 | 0.4428 | 0.9358 |
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- | 0.1007 | 1.19 | 2500 | 0.4344 | 0.9369 |
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- | 0.0655 | 1.21 | 2550 | 0.3215 | 0.9369 |
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- | 0.074 | 1.24 | 2600 | 0.3182 | 0.9404 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9231651376146789
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  ---
27
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the glue dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2179
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+ - Accuracy: 0.9232
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 0.6384 | 0.02 | 50 | 0.6360 | 0.7064 |
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+ | 0.3416 | 0.05 | 100 | 0.2955 | 0.8922 |
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+ | 0.29 | 0.07 | 150 | 0.2512 | 0.9094 |
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+ | 0.2371 | 0.1 | 200 | 0.2511 | 0.9106 |
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+ | 0.2059 | 0.12 | 250 | 0.2379 | 0.9174 |
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+ | 0.2617 | 0.14 | 300 | 0.2299 | 0.9174 |
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+ | 0.2266 | 0.17 | 350 | 0.2190 | 0.9243 |
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+ | 0.2288 | 0.19 | 400 | 0.2292 | 0.9255 |
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+ | 0.2385 | 0.21 | 450 | 0.2263 | 0.9232 |
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+ | 0.161 | 0.24 | 500 | 0.2368 | 0.9243 |
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+ | 0.158 | 0.26 | 550 | 0.2411 | 0.9174 |
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+ | 0.2469 | 0.29 | 600 | 0.2381 | 0.9209 |
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+ | 0.2417 | 0.31 | 650 | 0.2349 | 0.9163 |
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+ | 0.1614 | 0.33 | 700 | 0.2251 | 0.9174 |
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+ | 0.2764 | 0.36 | 750 | 0.2129 | 0.9266 |
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+ | 0.1499 | 0.38 | 800 | 0.2248 | 0.9197 |
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+ | 0.1376 | 0.4 | 850 | 0.2285 | 0.9232 |
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+ | 0.1875 | 0.43 | 900 | 0.2324 | 0.9312 |
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+ | 0.1819 | 0.45 | 950 | 0.2302 | 0.9220 |
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+ | 0.2373 | 0.48 | 1000 | 0.2179 | 0.9232 |
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+ | 0.0956 | 0.5 | 1050 | 0.2077 | 0.9278 |
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+ | 0.2396 | 0.52 | 1100 | 0.3249 | 0.9266 |
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+ | 0.2543 | 0.55 | 1150 | 0.4440 | 0.9243 |
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+ | 0.0942 | 0.57 | 1200 | 0.1982 | 0.9312 |
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+ | 0.1296 | 0.59 | 1250 | 0.4270 | 0.9335 |
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+ | 0.1618 | 0.62 | 1300 | 0.1893 | 0.9392 |
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+ | 0.1902 | 0.64 | 1350 | 0.1911 | 0.9381 |
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+ | 0.1234 | 0.67 | 1400 | 0.1903 | 0.9346 |
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+ | 0.1369 | 0.69 | 1450 | 0.4157 | 0.9335 |
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+ | 0.1149 | 0.71 | 1500 | 0.4121 | 0.9323 |
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+ | 0.1501 | 0.74 | 1550 | 0.6343 | 0.9358 |
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+ | 0.1679 | 0.76 | 1600 | 0.5294 | 0.9323 |
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+ | 0.1462 | 0.78 | 1650 | 0.4037 | 0.9392 |
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+ | 0.2111 | 0.81 | 1700 | 0.4094 | 0.9323 |
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+ | 0.0902 | 0.83 | 1750 | 0.4094 | 0.9346 |
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+ | 0.1185 | 0.86 | 1800 | 0.4059 | 0.9323 |
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+ | 0.1602 | 0.88 | 1850 | 0.2946 | 0.9323 |
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+ | 0.1212 | 0.9 | 1900 | 0.3037 | 0.9312 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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