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CNEC2_0_Supertypes_xlm-roberta-large

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  1. README.md +23 -32
  2. model.safetensors +1 -1
README.md CHANGED
@@ -1,6 +1,6 @@
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  ---
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- license: mit
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- base_model: FacebookAI/xlm-roberta-large
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  tags:
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  - generated_from_trainer
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  datasets:
@@ -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: 0.8311333636777424
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  - name: Recall
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  type: recall
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- value: 0.8812741312741312
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  - name: F1
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  type: f1
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- value: 0.8554696650269384
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  - name: Accuracy
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  type: accuracy
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- value: 0.9682167173692597
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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
@@ -42,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # CNEC2_0_Supertypes_xlm-roberta-large
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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: 0.1827
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- - Precision: 0.8311
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- - Recall: 0.8813
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- - F1: 0.8555
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- - Accuracy: 0.9682
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  ## Model description
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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: 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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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 1.0633 | 0.56 | 500 | 0.2514 | 0.4967 | 0.6544 | 0.5648 | 0.9322 |
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- | 0.2454 | 1.11 | 1000 | 0.2044 | 0.6504 | 0.7901 | 0.7134 | 0.9532 |
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- | 0.2064 | 1.67 | 1500 | 0.1721 | 0.7254 | 0.7828 | 0.7530 | 0.9562 |
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- | 0.1698 | 2.22 | 2000 | 0.1755 | 0.7472 | 0.8388 | 0.7904 | 0.9604 |
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- | 0.1472 | 2.78 | 2500 | 0.1478 | 0.7547 | 0.8417 | 0.7958 | 0.9624 |
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- | 0.1244 | 3.33 | 3000 | 0.1516 | 0.7934 | 0.8412 | 0.8166 | 0.9638 |
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- | 0.12 | 3.89 | 3500 | 0.1366 | 0.7851 | 0.8692 | 0.8250 | 0.9665 |
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- | 0.0946 | 4.44 | 4000 | 0.1678 | 0.7815 | 0.8494 | 0.8141 | 0.9652 |
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- | 0.1024 | 5.0 | 4500 | 0.1389 | 0.7756 | 0.8509 | 0.8115 | 0.9649 |
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- | 0.0765 | 5.56 | 5000 | 0.1563 | 0.7824 | 0.8571 | 0.8181 | 0.9663 |
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- | 0.0802 | 6.11 | 5500 | 0.1677 | 0.8024 | 0.8586 | 0.8296 | 0.9646 |
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- | 0.0612 | 6.67 | 6000 | 0.1723 | 0.8068 | 0.8769 | 0.8404 | 0.9662 |
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- | 0.0529 | 7.22 | 6500 | 0.1698 | 0.8230 | 0.8774 | 0.8493 | 0.9686 |
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- | 0.0476 | 7.78 | 7000 | 0.1648 | 0.8271 | 0.8702 | 0.8481 | 0.9689 |
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- | 0.0487 | 8.33 | 7500 | 0.1721 | 0.8287 | 0.8707 | 0.8491 | 0.9683 |
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- | 0.0392 | 8.89 | 8000 | 0.1787 | 0.8222 | 0.8769 | 0.8487 | 0.9681 |
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- | 0.0361 | 9.44 | 8500 | 0.1803 | 0.8392 | 0.8818 | 0.8600 | 0.9682 |
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- | 0.034 | 10.0 | 9000 | 0.1827 | 0.8311 | 0.8813 | 0.8555 | 0.9682 |
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  ### Framework versions
 
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  ---
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+ license: apache-2.0
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+ base_model: distilbert/distilbert-base-multilingual-cased
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.7557829181494662
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  - name: Recall
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  type: recall
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+ value: 0.819980694980695
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  - name: F1
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  type: f1
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+ value: 0.7865740740740742
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9568269568269568
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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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  # CNEC2_0_Supertypes_xlm-roberta-large
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+ This model is a fine-tuned version of [distilbert/distilbert-base-multilingual-cased](https://huggingface.co/distilbert/distilbert-base-multilingual-cased) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2049
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+ - Precision: 0.7558
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+ - Recall: 0.8200
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+ - F1: 0.7866
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+ - Accuracy: 0.9568
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  ## Model description
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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: 16
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+ - eval_batch_size: 16
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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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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.7025 | 1.11 | 500 | 0.2950 | 0.5066 | 0.5927 | 0.5463 | 0.9128 |
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+ | 0.2152 | 2.22 | 1000 | 0.2057 | 0.6733 | 0.7539 | 0.7113 | 0.9425 |
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+ | 0.1366 | 3.33 | 1500 | 0.1680 | 0.7228 | 0.7891 | 0.7545 | 0.9525 |
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+ | 0.0849 | 4.44 | 2000 | 0.1710 | 0.7246 | 0.7987 | 0.7599 | 0.9540 |
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+ | 0.0574 | 5.56 | 2500 | 0.1725 | 0.7309 | 0.8166 | 0.7714 | 0.9558 |
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+ | 0.0384 | 6.67 | 3000 | 0.1855 | 0.7327 | 0.8243 | 0.7758 | 0.9554 |
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+ | 0.0292 | 7.78 | 3500 | 0.1944 | 0.7557 | 0.8287 | 0.7905 | 0.9573 |
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+ | 0.0208 | 8.89 | 4000 | 0.2053 | 0.7486 | 0.8118 | 0.7789 | 0.9555 |
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+ | 0.0164 | 10.0 | 4500 | 0.2049 | 0.7558 | 0.8200 | 0.7866 | 0.9568 |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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