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lobrien001
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roberta-base-finetuned-ner

Token Classification
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roberta
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  1. README.md +40 -21
  2. model.safetensors +1 -1
  3. tokenizer.json +16 -2
  4. training_args.bin +2 -2
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5509
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- - Precision: 0.8574
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- - Recall: 0.9049
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- - F1: 0.8805
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- - Accuracy: 0.8574
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  ## Model description
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@@ -44,30 +44,49 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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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: 2
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 0.15 | 10 | 0.5834 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 0.3 | 20 | 0.5566 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 0.45 | 30 | 0.5712 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 0.6 | 40 | 0.5596 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 0.75 | 50 | 0.5566 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 0.9 | 60 | 0.5554 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.04 | 70 | 0.5671 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.19 | 80 | 0.5510 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.34 | 90 | 0.5534 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.49 | 100 | 0.5526 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.64 | 110 | 0.5583 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.79 | 120 | 0.5507 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
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- | No log | 1.94 | 130 | 0.5509 | 0.8574 | 0.9049 | 0.8805 | 0.8574 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.1185
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+ - Precision: 0.7791
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+ - Recall: 0.8034
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+ - F1: 0.7910
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+ - Accuracy: 0.7680
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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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: 6
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 0.19 | 50 | 1.2727 | 0.6213 | 0.4935 | 0.5501 | 0.4934 |
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+ | No log | 0.37 | 100 | 1.2623 | 0.6398 | 0.5312 | 0.5805 | 0.5263 |
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+ | No log | 0.56 | 150 | 1.2519 | 0.6609 | 0.5693 | 0.6117 | 0.5593 |
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+ | No log | 0.75 | 200 | 1.2423 | 0.6713 | 0.5940 | 0.6303 | 0.5815 |
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+ | No log | 0.93 | 250 | 1.2330 | 0.6828 | 0.6167 | 0.6481 | 0.6014 |
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+ | No log | 1.12 | 300 | 1.2241 | 0.6914 | 0.6388 | 0.6640 | 0.6219 |
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+ | No log | 1.31 | 350 | 1.2158 | 0.6962 | 0.6540 | 0.6744 | 0.6350 |
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+ | No log | 1.49 | 400 | 1.2076 | 0.6995 | 0.6637 | 0.6811 | 0.6434 |
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+ | No log | 1.68 | 450 | 1.2000 | 0.7048 | 0.6767 | 0.6905 | 0.6545 |
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+ | 1.2539 | 1.87 | 500 | 1.1926 | 0.7093 | 0.6880 | 0.6985 | 0.6645 |
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+ | 1.2539 | 2.05 | 550 | 1.1859 | 0.7148 | 0.6990 | 0.7068 | 0.6736 |
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+ | 1.2539 | 2.24 | 600 | 1.1793 | 0.7206 | 0.7092 | 0.7148 | 0.6824 |
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+ | 1.2539 | 2.43 | 650 | 1.1733 | 0.7269 | 0.7209 | 0.7239 | 0.6935 |
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+ | 1.2539 | 2.61 | 700 | 1.1676 | 0.7340 | 0.7306 | 0.7323 | 0.7025 |
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+ | 1.2539 | 2.8 | 750 | 1.1620 | 0.7385 | 0.7380 | 0.7382 | 0.7091 |
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+ | 1.2539 | 2.99 | 800 | 1.1569 | 0.7429 | 0.7451 | 0.744 | 0.7160 |
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+ | 1.2539 | 3.17 | 850 | 1.1521 | 0.7496 | 0.7560 | 0.7528 | 0.7265 |
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+ | 1.2539 | 3.36 | 900 | 1.1476 | 0.7539 | 0.7622 | 0.7580 | 0.7325 |
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+ | 1.2539 | 3.54 | 950 | 1.1435 | 0.7552 | 0.7657 | 0.7604 | 0.7349 |
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+ | 1.1751 | 3.73 | 1000 | 1.1399 | 0.7585 | 0.7718 | 0.7651 | 0.7405 |
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+ | 1.1751 | 3.92 | 1050 | 1.1364 | 0.7626 | 0.7789 | 0.7706 | 0.7470 |
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+ | 1.1751 | 4.1 | 1100 | 1.1332 | 0.7657 | 0.7835 | 0.7745 | 0.7513 |
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+ | 1.1751 | 4.29 | 1150 | 1.1303 | 0.7700 | 0.7895 | 0.7796 | 0.7561 |
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+ | 1.1751 | 4.48 | 1200 | 1.1278 | 0.7727 | 0.7934 | 0.7829 | 0.7589 |
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+ | 1.1751 | 4.66 | 1250 | 1.1256 | 0.7732 | 0.7945 | 0.7837 | 0.7600 |
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+ | 1.1751 | 4.85 | 1300 | 1.1237 | 0.7744 | 0.7960 | 0.7851 | 0.7614 |
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+ | 1.1751 | 5.04 | 1350 | 1.1221 | 0.7748 | 0.7973 | 0.7859 | 0.7622 |
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+ | 1.1751 | 5.22 | 1400 | 1.1208 | 0.7766 | 0.7995 | 0.7879 | 0.7643 |
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+ | 1.1751 | 5.41 | 1450 | 1.1198 | 0.7783 | 0.8021 | 0.7900 | 0.7665 |
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+ | 1.1363 | 5.6 | 1500 | 1.1191 | 0.7789 | 0.8032 | 0.7908 | 0.7675 |
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+ | 1.1363 | 5.78 | 1550 | 1.1187 | 0.7791 | 0.8034 | 0.7910 | 0.7680 |
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+ | 1.1363 | 5.97 | 1600 | 1.1185 | 0.7791 | 0.8034 | 0.7910 | 0.7680 |
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  ### Framework versions
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