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xlnetFv4_ftis_noPretrain

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  1. README.md +155 -0
  2. model.safetensors +1 -1
  3. runs/0-sample_rate=0.2/events.out.tfevents.1739260257.yara2.1274861.1 +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: xlnetFv4_ftis_noPretrain
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+ results: []
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+ ---
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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
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # xlnetFv4_ftis_noPretrain
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+
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+ This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.7658
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+ - Accuracy: 0.4289
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+ - Macro F1: 0.1717
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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+ - train_batch_size: 1
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+ - eval_batch_size: 1
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 53850
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+ - training_steps: 1077000
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
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+ |:-------------:|:-------:|:-----:|:---------------:|:--------:|:--------:|
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+ | 3.2778 | 0.0009 | 1000 | 3.4101 | 0.1492 | 0.0508 |
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+ | 2.6169 | 1.0009 | 2000 | 3.0657 | 0.3318 | 0.0747 |
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+ | 2.0767 | 2.0008 | 3000 | 2.5978 | 0.3767 | 0.0775 |
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+ | 1.9754 | 3.0007 | 4000 | 2.4066 | 0.4042 | 0.0882 |
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+ | 2.0542 | 4.0006 | 5000 | 2.2766 | 0.4015 | 0.0917 |
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+ | 1.8771 | 5.0006 | 6000 | 2.2322 | 0.4058 | 0.1041 |
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+ | 1.9728 | 6.0005 | 7000 | 2.1515 | 0.3971 | 0.1038 |
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+ | 1.9356 | 7.0004 | 8000 | 2.1180 | 0.4048 | 0.1076 |
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+ | 1.8861 | 8.0004 | 9000 | 2.0644 | 0.4044 | 0.1101 |
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+ | 1.8417 | 9.0003 | 10000 | 2.0713 | 0.3948 | 0.1095 |
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+ | 1.9464 | 10.0002 | 11000 | 2.0515 | 0.3801 | 0.1081 |
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+ | 1.9529 | 11.0001 | 12000 | 2.0508 | 0.3754 | 0.1055 |
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+ | 1.8001 | 12.0001 | 13000 | 2.0173 | 0.3900 | 0.1110 |
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+ | 1.895 | 12.0010 | 14000 | 2.0202 | 0.3920 | 0.1123 |
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+ | 1.9938 | 13.0009 | 15000 | 1.9929 | 0.3601 | 0.1032 |
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+ | 1.9619 | 14.0009 | 16000 | 1.9595 | 0.3882 | 0.1153 |
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+ | 1.9526 | 15.0008 | 17000 | 1.9886 | 0.3566 | 0.1079 |
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+ | 1.792 | 16.0007 | 18000 | 1.8701 | 0.3935 | 0.1162 |
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+ | 1.7947 | 17.0006 | 19000 | 1.9231 | 0.4008 | 0.1267 |
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+ | 1.964 | 18.0006 | 20000 | 1.9059 | 0.4176 | 0.1343 |
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+ | 1.8604 | 19.0005 | 21000 | 1.8850 | 0.3899 | 0.1239 |
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+ | 1.9679 | 20.0004 | 22000 | 1.8949 | 0.4385 | 0.1398 |
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+ | 1.9046 | 21.0004 | 23000 | 1.8807 | 0.3680 | 0.1262 |
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+ | 1.786 | 22.0003 | 24000 | 1.8673 | 0.4111 | 0.1437 |
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+ | 1.902 | 23.0002 | 25000 | 1.8611 | 0.4282 | 0.1429 |
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+ | 2.0151 | 24.0001 | 26000 | 1.8620 | 0.3950 | 0.1350 |
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+ | 1.8067 | 25.0001 | 27000 | 1.8583 | 0.4070 | 0.1387 |
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+ | 1.7885 | 25.0010 | 28000 | 1.8710 | 0.4190 | 0.1427 |
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+ | 1.9458 | 26.0009 | 29000 | 1.8955 | 0.4172 | 0.1382 |
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+ | 1.8265 | 27.0009 | 30000 | 1.8044 | 0.4128 | 0.1385 |
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+ | 1.8682 | 28.0008 | 31000 | 1.8223 | 0.3833 | 0.1313 |
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+ | 1.8845 | 29.0007 | 32000 | 1.8387 | 0.3893 | 0.1348 |
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+ | 1.8141 | 30.0006 | 33000 | 1.8078 | 0.4263 | 0.1465 |
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+ | 1.9524 | 31.0006 | 34000 | 1.8476 | 0.4008 | 0.1424 |
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+ | 1.8705 | 32.0005 | 35000 | 1.7960 | 0.3955 | 0.1365 |
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+ | 1.9233 | 33.0004 | 36000 | 1.8082 | 0.4222 | 0.1515 |
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+ | 1.7887 | 34.0004 | 37000 | 1.8289 | 0.4197 | 0.1392 |
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+ | 1.8995 | 35.0003 | 38000 | 1.8094 | 0.3644 | 0.1302 |
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+ | 1.9193 | 36.0002 | 39000 | 1.7976 | 0.4105 | 0.1333 |
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+ | 1.807 | 37.0001 | 40000 | 1.7958 | 0.3994 | 0.1258 |
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+ | 1.6897 | 38.0001 | 41000 | 1.8255 | 0.4009 | 0.1400 |
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+ | 1.8465 | 38.0010 | 42000 | 1.8031 | 0.3839 | 0.1352 |
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+ | 1.9148 | 39.0009 | 43000 | 1.8039 | 0.3886 | 0.1399 |
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+ | 1.9204 | 40.0009 | 44000 | 1.8456 | 0.3819 | 0.1312 |
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+ | 1.8785 | 41.0008 | 45000 | 1.8408 | 0.4060 | 0.1466 |
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+ | 1.9642 | 42.0007 | 46000 | 1.8162 | 0.3727 | 0.1427 |
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+ | 1.9356 | 43.0006 | 47000 | 1.8446 | 0.4005 | 0.1513 |
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+ | 1.7817 | 44.0006 | 48000 | 1.7895 | 0.4063 | 0.1453 |
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+ | 1.8289 | 45.0005 | 49000 | 1.7917 | 0.4138 | 0.1392 |
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+ | 1.8518 | 46.0004 | 50000 | 1.8293 | 0.4007 | 0.1589 |
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+ | 1.7782 | 47.0004 | 51000 | 1.7812 | 0.4088 | 0.1355 |
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+ | 1.9633 | 48.0003 | 52000 | 1.8348 | 0.4056 | 0.1514 |
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+ | 1.9718 | 49.0002 | 53000 | 1.7777 | 0.4426 | 0.1595 |
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+ | 1.9248 | 50.0001 | 54000 | 1.7779 | 0.4174 | 0.1501 |
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+ | 1.7681 | 51.0001 | 55000 | 1.7986 | 0.4014 | 0.1368 |
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+ | 1.8174 | 51.0010 | 56000 | 1.8043 | 0.4056 | 0.1420 |
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+ | 1.841 | 52.0009 | 57000 | 1.7879 | 0.3974 | 0.1425 |
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+ | 1.8102 | 53.0009 | 58000 | 1.8208 | 0.4252 | 0.1552 |
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+ | 1.8059 | 54.0008 | 59000 | 1.7790 | 0.4062 | 0.1501 |
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+ | 1.8195 | 55.0007 | 60000 | 1.7846 | 0.4059 | 0.1538 |
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+ | 1.7883 | 56.0006 | 61000 | 1.7771 | 0.3950 | 0.1535 |
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+ | 1.8632 | 57.0006 | 62000 | 1.7695 | 0.4177 | 0.1601 |
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+ | 1.9495 | 58.0005 | 63000 | 1.7984 | 0.4275 | 0.1601 |
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+ | 1.9593 | 59.0004 | 64000 | 1.7376 | 0.4261 | 0.1488 |
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+ | 1.8409 | 60.0004 | 65000 | 1.7984 | 0.3857 | 0.1509 |
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+ | 1.8503 | 61.0003 | 66000 | 1.7980 | 0.3936 | 0.1593 |
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+ | 1.9144 | 62.0002 | 67000 | 1.7780 | 0.4075 | 0.1613 |
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+ | 1.8632 | 63.0001 | 68000 | 1.8192 | 0.4245 | 0.1565 |
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+ | 1.7526 | 64.0001 | 69000 | 1.7383 | 0.4045 | 0.1525 |
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+ | 1.6273 | 64.0010 | 70000 | 1.8099 | 0.4485 | 0.1603 |
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+ | 1.8939 | 65.0009 | 71000 | 1.7749 | 0.4344 | 0.1708 |
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+ | 1.8592 | 66.0009 | 72000 | 1.7911 | 0.3803 | 0.1374 |
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+ | 1.8739 | 67.0008 | 73000 | 1.7427 | 0.4399 | 0.1599 |
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+ | 1.7345 | 68.0007 | 74000 | 1.8108 | 0.4179 | 0.1396 |
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+ | 1.9316 | 69.0006 | 75000 | 1.7658 | 0.4289 | 0.1717 |
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+ | 1.7924 | 70.0006 | 76000 | 1.7827 | 0.4247 | 0.1662 |
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+ | 1.8339 | 71.0005 | 77000 | 1.7344 | 0.4266 | 0.1623 |
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+ | 1.9731 | 72.0004 | 78000 | 1.8000 | 0.3535 | 0.1539 |
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+ | 1.8868 | 73.0004 | 79000 | 1.7762 | 0.3975 | 0.1566 |
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+ | 1.8885 | 74.0003 | 80000 | 1.7581 | 0.4000 | 0.1642 |
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+ | 1.8781 | 75.0002 | 81000 | 1.8021 | 0.3695 | 0.1360 |
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+ | 1.9189 | 76.0001 | 82000 | 1.7375 | 0.4177 | 0.1551 |
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+ | 1.8382 | 77.0001 | 83000 | 1.8088 | 0.3697 | 0.1592 |
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+ | 1.828 | 77.0010 | 84000 | 1.7752 | 0.4315 | 0.1585 |
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+ | 1.8672 | 78.0009 | 85000 | 1.7715 | 0.4054 | 0.1684 |
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+ | 1.8834 | 79.0009 | 86000 | 1.7985 | 0.3988 | 0.1603 |
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+ | 1.783 | 80.0008 | 87000 | 1.7518 | 0.4374 | 0.1683 |
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+ | 1.8679 | 81.0007 | 88000 | 1.7966 | 0.3770 | 0.1549 |
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+ | 1.8818 | 82.0006 | 89000 | 1.7799 | 0.4094 | 0.1673 |
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+ | 1.7993 | 83.0006 | 90000 | 1.7827 | 0.3770 | 0.1504 |
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+ | 1.9272 | 84.0005 | 91000 | 1.7251 | 0.4290 | 0.1576 |
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+ | 1.8129 | 85.0004 | 92000 | 1.7738 | 0.3877 | 0.1580 |
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+ | 1.8326 | 86.0004 | 93000 | 1.7855 | 0.4101 | 0.1641 |
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+ | 1.9804 | 87.0003 | 94000 | 1.7172 | 0.4276 | 0.1676 |
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+ | 1.814 | 88.0002 | 95000 | 1.8198 | 0.3801 | 0.1560 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.0
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+ - Pytorch 2.3.1+cu121
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+ - Datasets 2.20.0
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+ - Tokenizers 0.20.1
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