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ramybaly
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ner_nerd_fine

Token Classification
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bert
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ramybaly commited on
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add model

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Files changed (6) hide show
  1. README.md +11 -16
  2. pytorch_model.bin +1 -1
  3. runs/Aug20_15-52-44_ip-172-31-23-147/1629474769.9432955/events.out.tfevents.1629474769.ip-172-31-23-147.10136.1 +3 -0
  4. runs/Aug20_15-52-44_ip-172-31-23-147/events.out.tfevents.1629474769.ip-172-31-23-147.10136.0 +3 -0
  5. runs/Aug20_15-52-44_ip-172-31-23-147/events.out.tfevents.1629479413.ip-172-31-23-147.10136.2 +3 -0
  6. training_args.bin +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model_index:
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  metric:
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  name: Accuracy
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  type: accuracy
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- value: 0.9058961278375514
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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
@@ -32,11 +32,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the nerd dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5332
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- - Precision: 0.6337
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- - Recall: 0.6731
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- - F1: 0.6528
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- - Accuracy: 0.9059
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  ## Model description
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@@ -68,16 +68,11 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.6337 | 1.0 | 8235 | 0.3391 | 0.5974 | 0.6567 | 0.6256 | 0.9010 |
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- | 0.3086 | 2.0 | 16470 | 0.3188 | 0.6276 | 0.6607 | 0.6437 | 0.9061 |
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- | 0.2394 | 3.0 | 24705 | 0.3304 | 0.6284 | 0.6740 | 0.6504 | 0.9064 |
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- | 0.1841 | 4.0 | 32940 | 0.3451 | 0.6286 | 0.6749 | 0.6509 | 0.9065 |
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- | 0.1392 | 5.0 | 41175 | 0.3837 | 0.6251 | 0.6745 | 0.6489 | 0.9056 |
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- | 0.1056 | 6.0 | 49410 | 0.4185 | 0.6307 | 0.6751 | 0.6521 | 0.9057 |
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- | 0.0812 | 7.0 | 57645 | 0.4615 | 0.6288 | 0.6774 | 0.6522 | 0.9052 |
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- | 0.0629 | 8.0 | 65880 | 0.4933 | 0.6332 | 0.6755 | 0.6537 | 0.9065 |
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- | 0.0492 | 9.0 | 74115 | 0.5266 | 0.6360 | 0.6752 | 0.6550 | 0.9067 |
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- | 0.0401 | 10.0 | 82350 | 0.5452 | 0.6340 | 0.6760 | 0.6543 | 0.9065 |
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  ### Framework versions
 
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  metric:
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  name: Accuracy
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  type: accuracy
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+ value: 0.9054809270602955
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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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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the nerd dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3336
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+ - Precision: 0.6298
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+ - Recall: 0.6766
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+ - F1: 0.6524
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+ - Accuracy: 0.9055
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.6155 | 1.0 | 8235 | 0.3365 | 0.6047 | 0.6575 | 0.6300 | 0.9016 |
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+ | 0.3067 | 2.0 | 16470 | 0.3174 | 0.6321 | 0.6641 | 0.6477 | 0.9065 |
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+ | 0.2379 | 3.0 | 24705 | 0.3271 | 0.6335 | 0.6794 | 0.6557 | 0.9073 |
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+ | 0.1835 | 4.0 | 32940 | 0.3449 | 0.6334 | 0.6755 | 0.6537 | 0.9066 |
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+ | 0.139 | 5.0 | 41175 | 0.3866 | 0.6355 | 0.6771 | 0.6556 | 0.9068 |
 
 
 
 
 
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  ### Framework versions
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