Training complete

README.md

@@ -3,6 +3,8 @@ license: mit
 base_model: xlm-roberta-base
 tags:
 - generated_from_trainer
+datasets:
+- id_nergrit_corpus
 metrics:
 - precision
 - recall
@@ -10,7 +12,29 @@ metrics:
 - accuracy
 model-index:
 - name: xlm-roberta-base-ner-silvanus
-  results: []
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: id_nergrit_corpus
+      type: id_nergrit_corpus
+      config: ner
+      split: validation
+      args: ner
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.918622848200313
+    - name: Recall
+      type: recall
+      value: 0.9280632411067193
+    - name: F1
+      type: f1
+      value: 0.9233189146677152
+    - name: Accuracy
+      type: accuracy
+      value: 0.9850887866850908
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -18,13 +42,13 @@ should probably proofread and complete it, then remove this comment. -->
 
 # xlm-roberta-base-ner-silvanus
 
-This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the id_nergrit_corpus dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0636
-- Precision: 0.9101
-- Recall: 0.9157
-- F1: 0.9129
-- Accuracy: 0.9853
+- Loss: 0.0635
+- Precision: 0.9186
+- Recall: 0.9281
+- F1: 0.9233
+- Accuracy: 0.9851
 
 ## Model description
 
@@ -43,7 +67,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 3e-05
+- learning_rate: 2e-05
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
@@ -55,9 +79,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1477        | 1.0   | 827  | 0.0558          | 0.8977    | 0.8826 | 0.8901 | 0.9837   |
-| 0.0465        | 2.0   | 1654 | 0.0590          | 0.9010    | 0.9032 | 0.9021 | 0.9842   |
-| 0.03          | 3.0   | 2481 | 0.0636          | 0.9101    | 0.9157 | 0.9129 | 0.9853   |
+| 0.1607        | 1.0   | 827  | 0.0519          | 0.9094    | 0.9249 | 0.9171 | 0.9855   |
+| 0.0464        | 2.0   | 1654 | 0.0545          | 0.9137    | 0.9289 | 0.9212 | 0.9849   |
+| 0.0322        | 3.0   | 2481 | 0.0635          | 0.9186    | 0.9281 | 0.9233 | 0.9851   |
 
 
 ### Framework versions