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stulcrad
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CNEC1_1_Supertypes_xlm-roberta-large

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xlm-roberta
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  1. README.md +22 -20
  2. model.safetensors +1 -1
  3. runs/Mar07_17-19-37_g05/events.out.tfevents.1709828378.g05.3355634.1 +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: 0.8365145228215768
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  - name: Recall
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  type: recall
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- value: 0.8912466843501327
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  - name: F1
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  type: f1
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- value: 0.863013698630137
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  - name: Accuracy
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  type: accuracy
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- value: 0.9635817166946407
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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: 0.1900
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- - Precision: 0.8365
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- - Recall: 0.8912
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- - F1: 0.8630
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- - Accuracy: 0.9636
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  ## Model description
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@@ -74,22 +74,24 @@ The following hyperparameters were used during training:
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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: 8
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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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- | 0.7817 | 0.85 | 500 | 0.2275 | 0.7073 | 0.7918 | 0.7472 | 0.9392 |
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- | 0.2438 | 1.7 | 1000 | 0.1940 | 0.7138 | 0.8324 | 0.7686 | 0.9493 |
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- | 0.1652 | 2.56 | 1500 | 0.1722 | 0.7951 | 0.8678 | 0.8298 | 0.9577 |
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- | 0.1346 | 3.41 | 2000 | 0.1706 | 0.8049 | 0.8811 | 0.8413 | 0.9593 |
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- | 0.107 | 4.26 | 2500 | 0.1750 | 0.7991 | 0.8793 | 0.8373 | 0.9611 |
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- | 0.0851 | 5.11 | 3000 | 0.1976 | 0.7964 | 0.8820 | 0.8370 | 0.9591 |
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- | 0.0711 | 5.96 | 3500 | 0.1763 | 0.8195 | 0.8793 | 0.8484 | 0.9623 |
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- | 0.0528 | 6.81 | 4000 | 0.1883 | 0.8341 | 0.8912 | 0.8617 | 0.9632 |
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- | 0.0475 | 7.67 | 4500 | 0.1900 | 0.8365 | 0.8912 | 0.8630 | 0.9636 |
 
 
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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: 0.831814415907208
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  - name: Recall
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  type: recall
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+ value: 0.887709991158267
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  - name: F1
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  type: f1
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+ value: 0.8588537211291701
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9631523478668176
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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: 0.1988
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+ - Precision: 0.8318
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+ - Recall: 0.8877
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+ - F1: 0.8589
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+ - Accuracy: 0.9632
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  ## Model description
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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: 1000
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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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+ | 1.0776 | 0.85 | 500 | 0.3123 | 0.5698 | 0.6799 | 0.6200 | 0.9204 |
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+ | 0.3031 | 1.7 | 1000 | 0.2037 | 0.7176 | 0.8143 | 0.7629 | 0.9474 |
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+ | 0.2204 | 2.56 | 1500 | 0.1951 | 0.7407 | 0.8400 | 0.7872 | 0.9496 |
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+ | 0.18 | 3.41 | 2000 | 0.1868 | 0.7400 | 0.8546 | 0.7932 | 0.9544 |
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+ | 0.1501 | 4.26 | 2500 | 0.1725 | 0.7852 | 0.8660 | 0.8236 | 0.9590 |
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+ | 0.1209 | 5.11 | 3000 | 0.1842 | 0.8026 | 0.8859 | 0.8422 | 0.9609 |
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+ | 0.1061 | 5.96 | 3500 | 0.1814 | 0.7875 | 0.8749 | 0.8289 | 0.9616 |
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+ | 0.0833 | 6.81 | 4000 | 0.1893 | 0.8163 | 0.8899 | 0.8515 | 0.9626 |
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+ | 0.0771 | 7.67 | 4500 | 0.1847 | 0.8244 | 0.8859 | 0.8540 | 0.9623 |
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+ | 0.0603 | 8.52 | 5000 | 0.1875 | 0.8297 | 0.8917 | 0.8596 | 0.9637 |
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+ | 0.0569 | 9.37 | 5500 | 0.1988 | 0.8318 | 0.8877 | 0.8589 | 0.9632 |
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  ### Framework versions
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