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lilt-roberta-DocLayNet-base_lines_ml256-v1

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  1. README.md +138 -0
  2. model.safetensors +1 -1
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+ ---
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+ license: mit
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+ base_model: nielsr/lilt-xlm-roberta-base
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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+ model-index:
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+ - name: lilt-roberta-DocLayNet-base_lines_ml256-v1
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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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+ # lilt-roberta-DocLayNet-base_lines_ml256-v1
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+
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+ This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9004
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+ - Precision: 0.8622
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+ - Recall: 0.8622
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+ - F1: 0.8622
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+ - Accuracy: 0.8622
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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: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 5
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.07 | 300 | 0.7371 | 0.6945 | 0.6945 | 0.6945 | 0.6945 |
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+ | 0.7701 | 0.14 | 600 | 0.8573 | 0.7488 | 0.7488 | 0.7488 | 0.7488 |
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+ | 0.7701 | 0.21 | 900 | 0.7687 | 0.7606 | 0.7606 | 0.7606 | 0.7606 |
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+ | 0.471 | 0.27 | 1200 | 0.7057 | 0.7750 | 0.7750 | 0.7750 | 0.7750 |
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+ | 0.4183 | 0.34 | 1500 | 0.6305 | 0.7961 | 0.7961 | 0.7961 | 0.7961 |
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+ | 0.4183 | 0.41 | 1800 | 0.7039 | 0.7769 | 0.7769 | 0.7769 | 0.7769 |
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+ | 0.3683 | 0.48 | 2100 | 0.5956 | 0.7980 | 0.7980 | 0.7980 | 0.7980 |
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+ | 0.3683 | 0.55 | 2400 | 0.7312 | 0.7864 | 0.7864 | 0.7864 | 0.7864 |
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+ | 0.3429 | 0.62 | 2700 | 0.5868 | 0.8049 | 0.8049 | 0.8049 | 0.8049 |
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+ | 0.3337 | 0.69 | 3000 | 0.5911 | 0.8010 | 0.8010 | 0.8010 | 0.8010 |
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+ | 0.3337 | 0.76 | 3300 | 0.7278 | 0.7893 | 0.7893 | 0.7893 | 0.7893 |
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+ | 0.3056 | 0.82 | 3600 | 0.8030 | 0.7908 | 0.7908 | 0.7908 | 0.7908 |
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+ | 0.3056 | 0.89 | 3900 | 0.6587 | 0.7978 | 0.7978 | 0.7978 | 0.7978 |
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+ | 0.2772 | 0.96 | 4200 | 0.5334 | 0.8315 | 0.8315 | 0.8315 | 0.8315 |
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+ | 0.2456 | 1.03 | 4500 | 0.6787 | 0.7992 | 0.7992 | 0.7992 | 0.7992 |
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+ | 0.2456 | 1.1 | 4800 | 0.7325 | 0.8037 | 0.8037 | 0.8037 | 0.8037 |
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+ | 0.2183 | 1.17 | 5100 | 0.7280 | 0.7985 | 0.7985 | 0.7985 | 0.7985 |
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+ | 0.2183 | 1.24 | 5400 | 0.9041 | 0.7787 | 0.7787 | 0.7787 | 0.7787 |
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+ | 0.2288 | 1.31 | 5700 | 0.7504 | 0.8076 | 0.8076 | 0.8076 | 0.8076 |
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+ | 0.2228 | 1.37 | 6000 | 0.6672 | 0.8042 | 0.8042 | 0.8042 | 0.8042 |
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+ | 0.2228 | 1.44 | 6300 | 0.5468 | 0.8511 | 0.8511 | 0.8511 | 0.8511 |
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+ | 0.1989 | 1.51 | 6600 | 0.5928 | 0.8229 | 0.8229 | 0.8229 | 0.8229 |
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+ | 0.1989 | 1.58 | 6900 | 0.6731 | 0.8150 | 0.8150 | 0.8150 | 0.8150 |
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+ | 0.2062 | 1.65 | 7200 | 0.7504 | 0.8030 | 0.8030 | 0.8030 | 0.8030 |
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+ | 0.1971 | 1.72 | 7500 | 0.6554 | 0.8255 | 0.8255 | 0.8255 | 0.8255 |
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+ | 0.1971 | 1.79 | 7800 | 0.7095 | 0.8046 | 0.8046 | 0.8046 | 0.8046 |
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+ | 0.1929 | 1.86 | 8100 | 0.6244 | 0.8397 | 0.8397 | 0.8397 | 0.8397 |
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+ | 0.1929 | 1.92 | 8400 | 0.8521 | 0.8067 | 0.8067 | 0.8067 | 0.8067 |
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+ | 0.1788 | 1.99 | 8700 | 0.7261 | 0.8088 | 0.8088 | 0.8088 | 0.8088 |
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+ | 0.1631 | 2.06 | 9000 | 0.6650 | 0.8272 | 0.8272 | 0.8272 | 0.8272 |
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+ | 0.1631 | 2.13 | 9300 | 0.8314 | 0.8142 | 0.8142 | 0.8142 | 0.8142 |
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+ | 0.1284 | 2.2 | 9600 | 0.9010 | 0.8113 | 0.8113 | 0.8113 | 0.8113 |
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+ | 0.1284 | 2.27 | 9900 | 0.9008 | 0.8087 | 0.8087 | 0.8087 | 0.8087 |
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+ | 0.1248 | 2.34 | 10200 | 0.9152 | 0.8065 | 0.8065 | 0.8065 | 0.8065 |
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+ | 0.1365 | 2.4 | 10500 | 0.6791 | 0.8393 | 0.8393 | 0.8393 | 0.8393 |
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+ | 0.1365 | 2.47 | 10800 | 0.7301 | 0.8185 | 0.8185 | 0.8185 | 0.8185 |
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+ | 0.1194 | 2.54 | 11100 | 0.8937 | 0.8050 | 0.8050 | 0.8050 | 0.8050 |
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+ | 0.1194 | 2.61 | 11400 | 0.7559 | 0.8293 | 0.8293 | 0.8293 | 0.8293 |
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+ | 0.1282 | 2.68 | 11700 | 0.7903 | 0.8163 | 0.8163 | 0.8163 | 0.8163 |
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+ | 0.1234 | 2.75 | 12000 | 1.0103 | 0.8090 | 0.8090 | 0.8090 | 0.8090 |
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+ | 0.1234 | 2.82 | 12300 | 0.9975 | 0.8096 | 0.8096 | 0.8096 | 0.8096 |
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+ | 0.1104 | 2.89 | 12600 | 0.8443 | 0.8171 | 0.8171 | 0.8171 | 0.8171 |
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+ | 0.1104 | 2.95 | 12900 | 0.8380 | 0.8125 | 0.8125 | 0.8125 | 0.8125 |
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+ | 0.1254 | 3.02 | 13200 | 0.8283 | 0.8223 | 0.8223 | 0.8223 | 0.8223 |
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+ | 0.0806 | 3.09 | 13500 | 0.9232 | 0.8323 | 0.8323 | 0.8323 | 0.8323 |
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+ | 0.0806 | 3.16 | 13800 | 1.0903 | 0.8176 | 0.8176 | 0.8176 | 0.8176 |
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+ | 0.0875 | 3.23 | 14100 | 1.0781 | 0.8110 | 0.8110 | 0.8110 | 0.8110 |
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+ | 0.0875 | 3.3 | 14400 | 0.8806 | 0.8277 | 0.8277 | 0.8277 | 0.8277 |
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+ | 0.0817 | 3.37 | 14700 | 1.0024 | 0.8212 | 0.8212 | 0.8212 | 0.8212 |
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+ | 0.085 | 3.44 | 15000 | 0.9078 | 0.8294 | 0.8294 | 0.8294 | 0.8294 |
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+ | 0.085 | 3.5 | 15300 | 0.8745 | 0.8377 | 0.8377 | 0.8377 | 0.8377 |
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+ | 0.0784 | 3.57 | 15600 | 0.9590 | 0.8329 | 0.8329 | 0.8329 | 0.8329 |
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+ | 0.0784 | 3.64 | 15900 | 0.8027 | 0.8500 | 0.8500 | 0.8500 | 0.8500 |
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+ | 0.0785 | 3.71 | 16200 | 1.0033 | 0.8171 | 0.8171 | 0.8171 | 0.8171 |
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+ | 0.0756 | 3.78 | 16500 | 0.8017 | 0.8446 | 0.8446 | 0.8446 | 0.8446 |
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+ | 0.0756 | 3.85 | 16800 | 1.0721 | 0.8162 | 0.8162 | 0.8162 | 0.8162 |
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+ | 0.078 | 3.92 | 17100 | 1.1095 | 0.8172 | 0.8172 | 0.8172 | 0.8172 |
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+ | 0.078 | 3.99 | 17400 | 1.0099 | 0.8200 | 0.8200 | 0.8200 | 0.8200 |
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+ | 0.0696 | 4.05 | 17700 | 1.0189 | 0.8249 | 0.8249 | 0.8249 | 0.8249 |
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+ | 0.0456 | 4.12 | 18000 | 1.2109 | 0.8165 | 0.8165 | 0.8165 | 0.8165 |
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+ | 0.0456 | 4.19 | 18300 | 1.0789 | 0.8273 | 0.8273 | 0.8273 | 0.8273 |
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+ | 0.0587 | 4.26 | 18600 | 1.0981 | 0.8277 | 0.8277 | 0.8277 | 0.8277 |
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+ | 0.0587 | 4.33 | 18900 | 1.0236 | 0.8395 | 0.8395 | 0.8395 | 0.8395 |
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+ | 0.0485 | 4.4 | 19200 | 0.9660 | 0.8381 | 0.8381 | 0.8381 | 0.8381 |
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+ | 0.056 | 4.47 | 19500 | 0.9447 | 0.8453 | 0.8453 | 0.8453 | 0.8453 |
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+ | 0.056 | 4.54 | 19800 | 0.9226 | 0.8564 | 0.8564 | 0.8564 | 0.8564 |
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+ | 0.0517 | 4.6 | 20100 | 1.1416 | 0.8313 | 0.8313 | 0.8313 | 0.8313 |
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+ | 0.0517 | 4.67 | 20400 | 0.9004 | 0.8622 | 0.8622 | 0.8622 | 0.8622 |
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+ | 0.0555 | 4.74 | 20700 | 1.0452 | 0.8416 | 0.8416 | 0.8416 | 0.8416 |
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+ | 0.0578 | 4.81 | 21000 | 0.9997 | 0.8480 | 0.8480 | 0.8480 | 0.8480 |
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+ | 0.0578 | 4.88 | 21300 | 1.0441 | 0.8402 | 0.8402 | 0.8402 | 0.8402 |
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+ | 0.0495 | 4.95 | 21600 | 1.0393 | 0.8421 | 0.8421 | 0.8421 | 0.8421 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.0
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+ - Pytorch 2.0.0
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+ - Datasets 2.1.0
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+ - Tokenizers 0.15.0
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