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xlm-roberta-base-ner-silvanus

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  1. README.md +13 -13
  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: 0.910221531286436
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  - name: Recall
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  type: recall
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- value: 0.9256916996047431
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  - name: F1
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  type: f1
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- value: 0.9178914364099547
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  - name: Accuracy
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  type: accuracy
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- value: 0.98449068822571
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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 [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the id_nergrit_corpus dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0635
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- - Precision: 0.9102
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- - Recall: 0.9257
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- - F1: 0.9179
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- - Accuracy: 0.9845
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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: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -79,9 +79,9 @@ 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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- | 0.167 | 1.0 | 827 | 0.0505 | 0.9025 | 0.9257 | 0.9140 | 0.9849 |
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- | 0.0465 | 2.0 | 1654 | 0.0545 | 0.9012 | 0.9300 | 0.9154 | 0.9837 |
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- | 0.0321 | 3.0 | 2481 | 0.0635 | 0.9102 | 0.9257 | 0.9179 | 0.9845 |
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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: 0.918918918918919
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  - name: Recall
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  type: recall
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+ value: 0.9272727272727272
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  - name: F1
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  type: f1
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+ value: 0.9230769230769231
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9858518778229216
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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 [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the id_nergrit_corpus dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0595
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+ - Precision: 0.9189
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+ - Recall: 0.9273
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+ - F1: 0.9231
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+ - Accuracy: 0.9859
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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: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.1394 | 1.0 | 827 | 0.0559 | 0.8808 | 0.9257 | 0.9027 | 0.9842 |
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+ | 0.0468 | 2.0 | 1654 | 0.0575 | 0.9107 | 0.9190 | 0.9148 | 0.9849 |
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+ | 0.0279 | 3.0 | 2481 | 0.0595 | 0.9189 | 0.9273 | 0.9231 | 0.9859 |
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  ### Framework versions
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