Training complete

README.md

@@ -3,8 +3,6 @@ license: mit
 base_model: xlm-roberta-base
 tags:
 - generated_from_trainer
-datasets:
-- id_nergrit_corpus
 metrics:
 - precision
 - recall
@@ -12,29 +10,7 @@ metrics:
 - accuracy
 model-index:
 - name: xlm-roberta-base-ner-silvanus
-  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: 0.9034110097939885
-    - name: Recall
-      type: recall
-      value: 0.9021922428330523
-    - name: F1
-      type: f1
-      value: 0.9028012149848127
-    - name: Accuracy
-      type: accuracy
-      value: 0.989266717325228
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -42,13 +18,13 @@ 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 id_nergrit_corpus dataset.
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0648
-- Precision: 0.9034
-- Recall: 0.9022
-- F1: 0.9028
-- Accuracy: 0.9893
+- Loss: 0.0636
+- Precision: 0.9101
+- Recall: 0.9157
+- F1: 0.9129
+- Accuracy: 0.9853
 
 ## Model description
 
@@ -67,7 +43,7 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- learning_rate: 1e-05
+- learning_rate: 3e-05
 - train_batch_size: 8
 - eval_batch_size: 8
 - seed: 42
@@ -79,9 +55,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.007         | 1.0   | 1567 | 0.0606          | 0.9110    | 0.8911 | 0.9009 | 0.9890   |
-| 0.0052        | 2.0   | 3134 | 0.0634          | 0.9066    | 0.8968 | 0.9017 | 0.9895   |
-| 0.0053        | 3.0   | 4701 | 0.0648          | 0.9034    | 0.9022 | 0.9028 | 0.9893   |
+| 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   |
 
 
 ### Framework versions