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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 +26 -21
  2. model.safetensors +1 -1
  3. runs/Mar07_18-36-10_g01/events.out.tfevents.1709832971.g01.769784.4 +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: 0.7760029717682021
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  - name: Recall
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  type: recall
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- value: 0.8582580115036976
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  - name: F1
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  type: f1
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- value: 0.8150604760046821
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  - name: Accuracy
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  type: accuracy
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- value: 0.9631292359381336
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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: 0.1727
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- - Precision: 0.7760
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- - Recall: 0.8583
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- - F1: 0.8151
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- - Accuracy: 0.9631
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  ## Model description
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@@ -67,29 +67,34 @@ 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: 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: 5
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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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- | 0.9465 | 0.56 | 500 | 0.2705 | 0.4955 | 0.6754 | 0.5716 | 0.9281 |
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- | 0.2305 | 1.11 | 1000 | 0.1836 | 0.7054 | 0.8205 | 0.7586 | 0.9539 |
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- | 0.179 | 1.67 | 1500 | 0.1784 | 0.7485 | 0.8180 | 0.7817 | 0.9576 |
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- | 0.1484 | 2.22 | 2000 | 0.1835 | 0.7571 | 0.8578 | 0.8043 | 0.9615 |
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- | 0.1283 | 2.78 | 2500 | 0.1792 | 0.7333 | 0.8135 | 0.7713 | 0.9596 |
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- | 0.1092 | 3.33 | 3000 | 0.1749 | 0.7707 | 0.8422 | 0.8049 | 0.9619 |
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- | 0.0963 | 3.89 | 3500 | 0.1706 | 0.7711 | 0.8537 | 0.8103 | 0.9633 |
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- | 0.0845 | 4.44 | 4000 | 0.1709 | 0.7811 | 0.8517 | 0.8149 | 0.9633 |
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- | 0.0801 | 5.0 | 4500 | 0.1727 | 0.7760 | 0.8583 | 0.8151 | 0.9631 |
 
 
 
 
 
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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: 0.8022359290670779
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  - name: Recall
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  type: recall
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+ value: 0.8549712407559573
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  - name: F1
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  type: f1
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+ value: 0.8277645186953062
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9616810519608411
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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: 0.2033
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+ - Precision: 0.8022
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+ - Recall: 0.8550
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+ - F1: 0.8278
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+ - Accuracy: 0.9617
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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: 8
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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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  - 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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+ | 0.6981 | 0.56 | 500 | 0.3042 | 0.5141 | 0.6652 | 0.5800 | 0.9121 |
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+ | 0.2782 | 1.11 | 1000 | 0.2128 | 0.7078 | 0.8159 | 0.7580 | 0.9495 |
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+ | 0.2247 | 1.67 | 1500 | 0.2200 | 0.7055 | 0.8081 | 0.7534 | 0.9450 |
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+ | 0.1986 | 2.22 | 2000 | 0.2291 | 0.6569 | 0.8110 | 0.7259 | 0.9460 |
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+ | 0.1697 | 2.78 | 2500 | 0.1819 | 0.7520 | 0.8184 | 0.7838 | 0.9548 |
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+ | 0.1415 | 3.33 | 3000 | 0.1873 | 0.7341 | 0.7975 | 0.7645 | 0.9527 |
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+ | 0.1284 | 3.89 | 3500 | 0.1752 | 0.7618 | 0.8578 | 0.8070 | 0.9590 |
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+ | 0.1073 | 4.44 | 4000 | 0.1903 | 0.7793 | 0.8488 | 0.8126 | 0.9586 |
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+ | 0.1006 | 5.0 | 4500 | 0.1741 | 0.7922 | 0.8661 | 0.8275 | 0.9610 |
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+ | 0.0788 | 5.56 | 5000 | 0.1830 | 0.7995 | 0.8537 | 0.8258 | 0.9623 |
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+ | 0.0838 | 6.11 | 5500 | 0.2096 | 0.8018 | 0.8509 | 0.8256 | 0.9610 |
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+ | 0.0617 | 6.67 | 6000 | 0.1978 | 0.8056 | 0.8632 | 0.8334 | 0.9627 |
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+ | 0.0515 | 7.22 | 6500 | 0.2020 | 0.8061 | 0.8521 | 0.8284 | 0.9616 |
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+ | 0.0455 | 7.78 | 7000 | 0.2033 | 0.8022 | 0.8550 | 0.8278 | 0.9617 |
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  ### Framework versions
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