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stulcrad
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CNEC1_1_extended_xlm-roberta-large

Token Classification
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xlm-roberta
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  1. README.md +20 -26
  2. config.json +2 -2
  3. model.safetensors +1 -1
  4. runs/Apr14_21-52-54_g04/events.out.tfevents.1713124377.g04.1046538.1 +3 -0
  5. training_args.bin +1 -1
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: 0.8456410256410256
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  - name: Recall
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  type: recall
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- value: 0.8813468733297701
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  - name: F1
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  type: f1
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- value: 0.8631248364302538
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  - name: Accuracy
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  type: accuracy
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- value: 0.9673435458971619
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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 [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2299
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- - Precision: 0.8456
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- - Recall: 0.8813
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- - F1: 0.8631
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- - Accuracy: 0.9673
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  ## Model description
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@@ -67,31 +67,25 @@ More information needed
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  ### Training hyperparameters
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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: 8
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- - eval_batch_size: 8
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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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- - lr_scheduler_warmup_ratio: 0.1
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- - lr_scheduler_warmup_steps: 500
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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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- | 0.5516 | 0.86 | 500 | 0.1912 | 0.7007 | 0.7857 | 0.7407 | 0.9493 |
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- | 0.2153 | 1.72 | 1000 | 0.1856 | 0.6609 | 0.7825 | 0.7166 | 0.9461 |
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- | 0.1389 | 2.58 | 1500 | 0.1711 | 0.7791 | 0.8445 | 0.8105 | 0.9574 |
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- | 0.1098 | 3.44 | 2000 | 0.1943 | 0.8171 | 0.8642 | 0.84 | 0.9608 |
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- | 0.0785 | 4.3 | 2500 | 0.2197 | 0.7919 | 0.8461 | 0.8181 | 0.9579 |
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- | 0.0619 | 5.16 | 3000 | 0.1877 | 0.8298 | 0.8883 | 0.8580 | 0.9660 |
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- | 0.043 | 6.02 | 3500 | 0.2185 | 0.8412 | 0.8803 | 0.8603 | 0.9656 |
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- | 0.0289 | 6.88 | 4000 | 0.1898 | 0.8422 | 0.8846 | 0.8629 | 0.9674 |
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- | 0.0179 | 7.75 | 4500 | 0.2061 | 0.8433 | 0.8830 | 0.8627 | 0.9674 |
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- | 0.0112 | 8.61 | 5000 | 0.2218 | 0.8462 | 0.8819 | 0.8636 | 0.9656 |
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- | 0.0074 | 9.47 | 5500 | 0.2299 | 0.8456 | 0.8813 | 0.8631 | 0.9673 |
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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: 0.843939393939394
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  - name: Recall
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  type: recall
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+ value: 0.8767051416579223
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  - name: F1
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  type: f1
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+ value: 0.8600102933607823
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9590540063536886
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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 [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2330
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+ - Precision: 0.8439
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+ - Recall: 0.8767
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+ - F1: 0.8600
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+ - Accuracy: 0.9591
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  ## Model description
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  ### Training hyperparameters
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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: 32
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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: 25
 
 
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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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+ | 0.6037 | 3.42 | 500 | 0.2144 | 0.6741 | 0.7263 | 0.6993 | 0.9407 |
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+ | 0.2517 | 6.85 | 1000 | 0.1774 | 0.7915 | 0.8381 | 0.8141 | 0.9575 |
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+ | 0.1854 | 10.27 | 1500 | 0.1633 | 0.8251 | 0.8621 | 0.8432 | 0.9632 |
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+ | 0.1476 | 13.7 | 2000 | 0.1610 | 0.8356 | 0.8803 | 0.8574 | 0.9664 |
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+ | 0.1248 | 17.12 | 2500 | 0.1657 | 0.8459 | 0.8830 | 0.8640 | 0.9682 |
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+ | 0.1086 | 20.55 | 3000 | 0.1671 | 0.8542 | 0.8862 | 0.8699 | 0.9683 |
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+ | 0.0985 | 23.97 | 3500 | 0.1610 | 0.8600 | 0.8931 | 0.8762 | 0.9700 |
 
 
 
 
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  ### Framework versions
config.json CHANGED
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  "id2label": {
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