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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 +20 -18
  2. model.safetensors +1 -1
  3. runs/Mar06_16-51-44_n28/events.out.tfevents.1709740320.n28.863430.3 +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: 0.8251895534962089
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  - name: Recall
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  type: recall
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- value: 0.8788694481830417
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  - name: F1
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  type: f1
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- value: 0.8511840104279819
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  - name: Accuracy
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  type: accuracy
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- value: 0.9608493696084937
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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: 0.2044
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- - Precision: 0.8252
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- - Recall: 0.8789
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- - F1: 0.8512
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- - Accuracy: 0.9608
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  ## Model description
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@@ -73,21 +73,23 @@ 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: 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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- | 0.4123 | 0.85 | 500 | 0.2026 | 0.7055 | 0.8255 | 0.7608 | 0.9474 |
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- | 0.196 | 1.7 | 1000 | 0.1791 | 0.7699 | 0.8573 | 0.8113 | 0.9543 |
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- | 0.145 | 2.56 | 1500 | 0.1962 | 0.7604 | 0.8430 | 0.7996 | 0.9533 |
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- | 0.1184 | 3.41 | 2000 | 0.1812 | 0.7897 | 0.8708 | 0.8282 | 0.9569 |
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- | 0.0959 | 4.26 | 2500 | 0.1788 | 0.7989 | 0.8681 | 0.8321 | 0.9601 |
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- | 0.0707 | 5.11 | 3000 | 0.1868 | 0.8106 | 0.8852 | 0.8462 | 0.9616 |
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- | 0.0561 | 5.96 | 3500 | 0.1988 | 0.8132 | 0.8730 | 0.8421 | 0.9596 |
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- | 0.0404 | 6.81 | 4000 | 0.2027 | 0.8268 | 0.8847 | 0.8548 | 0.9614 |
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- | 0.0383 | 7.67 | 4500 | 0.2044 | 0.8252 | 0.8789 | 0.8512 | 0.9608 |
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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: 0.8413379073756432
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  - name: Recall
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  type: recall
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+ value: 0.8802153432032301
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  - name: F1
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  type: f1
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+ value: 0.860337645253234
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9612787384363168
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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: 0.2313
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+ - Precision: 0.8413
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+ - Recall: 0.8802
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+ - F1: 0.8603
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+ - Accuracy: 0.9613
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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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+ - 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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+ | 0.7584 | 0.85 | 500 | 0.2266 | 0.6844 | 0.8044 | 0.7395 | 0.9401 |
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+ | 0.2143 | 1.7 | 1000 | 0.2301 | 0.7516 | 0.8268 | 0.7874 | 0.9447 |
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+ | 0.1472 | 2.56 | 1500 | 0.2024 | 0.7634 | 0.8452 | 0.8022 | 0.9535 |
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+ | 0.1109 | 3.41 | 2000 | 0.1845 | 0.7719 | 0.8686 | 0.8174 | 0.9571 |
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+ | 0.0843 | 4.26 | 2500 | 0.1853 | 0.8025 | 0.8694 | 0.8346 | 0.9585 |
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+ | 0.0576 | 5.11 | 3000 | 0.1976 | 0.8235 | 0.8896 | 0.8553 | 0.9599 |
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+ | 0.0403 | 5.96 | 3500 | 0.2000 | 0.8308 | 0.8766 | 0.8531 | 0.9596 |
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+ | 0.025 | 6.81 | 4000 | 0.2242 | 0.8304 | 0.8811 | 0.8550 | 0.9602 |
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+ | 0.0206 | 7.67 | 4500 | 0.2313 | 0.8413 | 0.8802 | 0.8603 | 0.9613 |
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  ### Framework versions
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