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stulcrad
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CNEC2_0_Supertypes_xlm-roberta-large

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xlm-roberta
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  1. README.md +20 -18
  2. model.safetensors +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: 0.82387923147301
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  - name: Recall
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  type: recall
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- value: 0.8692084942084942
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  - name: F1
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  type: f1
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- value: 0.8459370596524189
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  - name: Accuracy
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  type: accuracy
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- value: 0.9673185571490657
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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: 0.1697
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- - Precision: 0.8239
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- - Recall: 0.8692
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- - F1: 0.8459
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- - Accuracy: 0.9673
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  ## Model description
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@@ -67,7 +67,7 @@ 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: 2e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -75,19 +75,21 @@ The following hyperparameters were used during training:
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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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- | 0.7425 | 1.11 | 500 | 0.1666 | 0.6549 | 0.7814 | 0.7126 | 0.9549 |
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- | 0.1416 | 2.22 | 1000 | 0.1419 | 0.7585 | 0.8567 | 0.8046 | 0.9628 |
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- | 0.0883 | 3.33 | 1500 | 0.1513 | 0.8017 | 0.8644 | 0.8319 | 0.9657 |
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- | 0.06 | 4.44 | 2000 | 0.1414 | 0.8151 | 0.8552 | 0.8347 | 0.9669 |
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- | 0.0433 | 5.56 | 2500 | 0.1518 | 0.8121 | 0.8533 | 0.8322 | 0.9667 |
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- | 0.0262 | 6.67 | 3000 | 0.1580 | 0.8236 | 0.8697 | 0.8460 | 0.9684 |
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- | 0.0175 | 7.78 | 3500 | 0.1697 | 0.8239 | 0.8692 | 0.8459 | 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: 0.8074141048824593
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  - name: Recall
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  type: recall
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+ value: 0.861969111969112
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  - name: F1
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  type: f1
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+ value: 0.8338001867413634
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9655222367086774
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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: 0.2044
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+ - Precision: 0.8074
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+ - Recall: 0.8620
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+ - F1: 0.8338
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+ - Accuracy: 0.9655
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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: 5e-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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  - 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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+ | 0.5564 | 1.11 | 500 | 0.1852 | 0.6302 | 0.7558 | 0.6873 | 0.9502 |
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+ | 0.1786 | 2.22 | 1000 | 0.1552 | 0.6952 | 0.8069 | 0.7469 | 0.9568 |
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+ | 0.1219 | 3.33 | 1500 | 0.1665 | 0.6860 | 0.8214 | 0.7476 | 0.9577 |
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+ | 0.087 | 4.44 | 2000 | 0.1616 | 0.7572 | 0.8263 | 0.7902 | 0.9595 |
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+ | 0.0689 | 5.56 | 2500 | 0.1679 | 0.7670 | 0.8243 | 0.7946 | 0.9616 |
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+ | 0.0442 | 6.67 | 3000 | 0.1612 | 0.7346 | 0.8364 | 0.7822 | 0.9631 |
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+ | 0.0353 | 7.78 | 3500 | 0.1864 | 0.8099 | 0.8576 | 0.8331 | 0.9653 |
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+ | 0.0205 | 8.89 | 4000 | 0.1950 | 0.8026 | 0.8653 | 0.8328 | 0.9654 |
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+ | 0.0133 | 10.0 | 4500 | 0.2044 | 0.8074 | 0.8620 | 0.8338 | 0.9655 |
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  ### Framework versions
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