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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 +18 -18
  2. model.safetensors +1 -1
  3. runs/Mar07_16-58-42_g05/events.out.tfevents.1709827123.g05.3355634.0 +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: 0.8266722759781236
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  - name: Recall
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  type: recall
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- value: 0.8815612382234186
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  - name: F1
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  type: f1
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- value: 0.8532349109856708
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  - name: Accuracy
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  type: accuracy
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- value: 0.961747140793942
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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: 0.2009
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- - Precision: 0.8267
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- - Recall: 0.8816
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- - F1: 0.8532
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- - Accuracy: 0.9617
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  ## Model description
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@@ -81,15 +81,15 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.8866 | 0.85 | 500 | 0.2420 | 0.6729 | 0.7972 | 0.7298 | 0.9388 |
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- | 0.2429 | 1.7 | 1000 | 0.2028 | 0.7223 | 0.8331 | 0.7738 | 0.9491 |
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- | 0.1689 | 2.56 | 1500 | 0.1860 | 0.7606 | 0.8609 | 0.8077 | 0.9554 |
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- | 0.1427 | 3.41 | 2000 | 0.1791 | 0.7810 | 0.8672 | 0.8219 | 0.9548 |
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- | 0.1109 | 4.26 | 2500 | 0.1829 | 0.7876 | 0.8699 | 0.8267 | 0.9583 |
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- | 0.086 | 5.11 | 3000 | 0.2049 | 0.8042 | 0.8807 | 0.8407 | 0.9590 |
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- | 0.0706 | 5.96 | 3500 | 0.2008 | 0.8142 | 0.8730 | 0.8426 | 0.9600 |
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- | 0.0584 | 6.81 | 4000 | 0.1909 | 0.8253 | 0.8793 | 0.8514 | 0.9617 |
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- | 0.0512 | 7.67 | 4500 | 0.2009 | 0.8267 | 0.8816 | 0.8532 | 0.9617 |
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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: 0.8365145228215768
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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
 
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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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  | 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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