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stulcrad
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CNEC_1_1_Supertypes_Czert-B-base-cased

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bert
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  1. README.md +13 -13
  2. model.safetensors +1 -1
README.md CHANGED
@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8084322554236595
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  - name: Recall
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  type: recall
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- value: 0.8731211317418214
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  - name: F1
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  type: f1
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- value: 0.8395324123273114
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  - name: Accuracy
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  type: accuracy
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- value: 0.9469740634005763
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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
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [UWB-AIR/Czert-B-base-cased](https://huggingface.co/UWB-AIR/Czert-B-base-cased) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2163
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- - Precision: 0.8084
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- - Recall: 0.8731
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- - F1: 0.8395
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- - Accuracy: 0.9470
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  ## Model description
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@@ -78,10 +78,10 @@ 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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- | 0.3977 | 1.7 | 500 | 0.2358 | 0.7670 | 0.8338 | 0.7990 | 0.9361 |
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- | 0.1773 | 3.4 | 1000 | 0.2191 | 0.7945 | 0.8630 | 0.8273 | 0.9429 |
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- | 0.127 | 5.1 | 1500 | 0.2087 | 0.8094 | 0.8714 | 0.8393 | 0.9462 |
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- | 0.1007 | 6.8 | 2000 | 0.2163 | 0.8084 | 0.8731 | 0.8395 | 0.9470 |
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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: 0.8262336566849431
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  - name: Recall
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  type: recall
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+ value: 0.8660477453580901
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  - name: F1
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  type: f1
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+ value: 0.8456723505288151
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9473102785782901
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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 [UWB-AIR/Czert-B-base-cased](https://huggingface.co/UWB-AIR/Czert-B-base-cased) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2250
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+ - Precision: 0.8262
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+ - Recall: 0.8660
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+ - F1: 0.8457
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+ - Accuracy: 0.9473
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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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+ | 0.334 | 1.7 | 500 | 0.1982 | 0.8008 | 0.8475 | 0.8235 | 0.9420 |
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+ | 0.1182 | 3.4 | 1000 | 0.2127 | 0.8336 | 0.8638 | 0.8485 | 0.9461 |
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+ | 0.0655 | 5.1 | 1500 | 0.2164 | 0.8205 | 0.8630 | 0.8412 | 0.9470 |
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+ | 0.0404 | 6.8 | 2000 | 0.2250 | 0.8262 | 0.8660 | 0.8457 | 0.9473 |
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  ### Framework versions
model.safetensors CHANGED
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