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ytonthat
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drbert-7gb-finedtuned-ner

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  1. README.md +30 -20
  2. model.safetensors +1 -1
  3. runs/Mar01_14-50-54_6d7974edcc8d/events.out.tfevents.1709304655.6d7974edcc8d.11866.0 +2 -2
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.5186372745490982
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  - name: Recall
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  type: recall
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- value: 0.5758789497107254
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  - name: F1
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  type: f1
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- value: 0.5457612821594264
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  - name: Accuracy
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  type: accuracy
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- value: 0.8037554229934925
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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
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Dr-BERT/DrBERT-7GB](https://huggingface.co/Dr-BERT/DrBERT-7GB) on the quaero dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.2062
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- - Precision: 0.5186
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- - Recall: 0.5759
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- - F1: 0.5458
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- - Accuracy: 0.8038
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  ## Model description
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@@ -73,22 +73,32 @@ The following hyperparameters were used during training:
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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: 10
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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 | 1.0 | 105 | 0.7393 | 0.4462 | 0.4775 | 0.4613 | 0.7751 |
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- | No log | 2.0 | 210 | 0.7016 | 0.4637 | 0.5265 | 0.4931 | 0.7845 |
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- | No log | 3.0 | 315 | 0.8441 | 0.4777 | 0.5109 | 0.4938 | 0.7849 |
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- | No log | 4.0 | 420 | 0.9610 | 0.4857 | 0.5456 | 0.5139 | 0.7903 |
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- | 0.4073 | 5.0 | 525 | 1.0035 | 0.4788 | 0.5688 | 0.5199 | 0.7884 |
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- | 0.4073 | 6.0 | 630 | 1.0399 | 0.4872 | 0.5745 | 0.5273 | 0.7970 |
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- | 0.4073 | 7.0 | 735 | 1.1211 | 0.4868 | 0.5665 | 0.5237 | 0.7913 |
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- | 0.4073 | 8.0 | 840 | 1.1545 | 0.5124 | 0.5688 | 0.5391 | 0.8054 |
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- | 0.4073 | 9.0 | 945 | 1.2049 | 0.4973 | 0.5728 | 0.5324 | 0.7984 |
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- | 0.0272 | 10.0 | 1050 | 1.2062 | 0.5186 | 0.5759 | 0.5458 | 0.8038 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.5218963439132182
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  - name: Recall
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  type: recall
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+ value: 0.5781041388518025
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  - name: F1
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  type: f1
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+ value: 0.5485641891891893
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8009761388286334
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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 [Dr-BERT/DrBERT-7GB](https://huggingface.co/Dr-BERT/DrBERT-7GB) on the quaero dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.4328
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+ - Precision: 0.5219
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+ - Recall: 0.5781
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+ - F1: 0.5486
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+ - Accuracy: 0.8010
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  ## Model description
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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: 20
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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 | 1.0 | 105 | 0.7200 | 0.4493 | 0.4833 | 0.4657 | 0.7785 |
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+ | No log | 2.0 | 210 | 0.7287 | 0.4381 | 0.5056 | 0.4694 | 0.7791 |
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+ | No log | 3.0 | 315 | 0.8443 | 0.4635 | 0.5207 | 0.4905 | 0.7823 |
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+ | No log | 4.0 | 420 | 1.0059 | 0.4780 | 0.5269 | 0.5013 | 0.7835 |
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+ | 0.4225 | 5.0 | 525 | 0.9944 | 0.4688 | 0.5545 | 0.5081 | 0.7806 |
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+ | 0.4225 | 6.0 | 630 | 1.0721 | 0.5104 | 0.5367 | 0.5232 | 0.7944 |
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+ | 0.4225 | 7.0 | 735 | 1.1099 | 0.4829 | 0.5639 | 0.5202 | 0.7933 |
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+ | 0.4225 | 8.0 | 840 | 1.2544 | 0.4814 | 0.5527 | 0.5146 | 0.7779 |
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+ | 0.4225 | 9.0 | 945 | 1.2083 | 0.4823 | 0.5639 | 0.5199 | 0.7905 |
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+ | 0.0481 | 10.0 | 1050 | 1.3132 | 0.4892 | 0.5621 | 0.5231 | 0.7924 |
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+ | 0.0481 | 11.0 | 1155 | 1.3484 | 0.5040 | 0.5665 | 0.5334 | 0.7942 |
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+ | 0.0481 | 12.0 | 1260 | 1.3297 | 0.5042 | 0.5585 | 0.5300 | 0.7989 |
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+ | 0.0481 | 13.0 | 1365 | 1.3890 | 0.5023 | 0.5741 | 0.5358 | 0.7955 |
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+ | 0.0481 | 14.0 | 1470 | 1.4140 | 0.5111 | 0.5639 | 0.5362 | 0.7985 |
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+ | 0.009 | 15.0 | 1575 | 1.4130 | 0.5226 | 0.5550 | 0.5383 | 0.7996 |
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+ | 0.009 | 16.0 | 1680 | 1.4197 | 0.5127 | 0.5639 | 0.5371 | 0.7964 |
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+ | 0.009 | 17.0 | 1785 | 1.4166 | 0.5262 | 0.5723 | 0.5483 | 0.8015 |
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+ | 0.009 | 18.0 | 1890 | 1.4257 | 0.5173 | 0.5781 | 0.5460 | 0.8004 |
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+ | 0.009 | 19.0 | 1995 | 1.4318 | 0.5203 | 0.5817 | 0.5493 | 0.8011 |
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+ | 0.0018 | 20.0 | 2100 | 1.4328 | 0.5219 | 0.5781 | 0.5486 | 0.8010 |
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  ### Framework versions
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