Training in progress, epoch 1

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: 0.8954509177972865
+    - name: Recall
+      type: recall
+      value: 0.8834645669291339
+    - name: F1
+      type: f1
+      value: 0.8894173602853745
+    - name: Accuracy
+      type: accuracy
+      value: 0.9820464165815209
 ---
 
 <!-- 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: 0.0885
-- Precision: 0.9374
-- Recall: 0.9485
-- F1: 0.9429
-- Accuracy: 0.9756
+- Loss: 0.0942
+- Precision: 0.8955
+- Recall: 0.8835
+- F1: 0.8894
+- Accuracy: 0.9820
 
 ## Model description
 
@@ -44,11 +68,9 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 6
+- train_batch_size: 2
 - eval_batch_size: 8
 - seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 24
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - num_epochs: 3
@@ -57,9 +79,9 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1123        | 1.0   | 1560 | 0.0853          | 0.9090    | 0.9388 | 0.9237 | 0.9705   |
-| 0.0734        | 2.0   | 3121 | 0.0914          | 0.9303    | 0.9440 | 0.9371 | 0.9741   |
-| 0.0494        | 3.0   | 4680 | 0.0885          | 0.9374    | 0.9485 | 0.9429 | 0.9756   |
+| 0.0955        | 1.0   | 2285 | 0.0766          | 0.8528    | 0.8669 | 0.8598 | 0.9813   |
+| 0.0558        | 2.0   | 4570 | 0.0860          | 0.8867    | 0.8693 | 0.8779 | 0.9811   |
+| 0.0368        | 3.0   | 6855 | 0.0942          | 0.8955    | 0.8835 | 0.8894 | 0.9820   |
 
 
 ### Framework versions

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training_args.bin

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