Model save

README.md

@@ -25,16 +25,16 @@ model-index:
     metrics:
     - name: Precision
       type: precision
-      value: 0.8532910388580491
+      value: 0.8615819209039548
     - name: Recall
       type: recall
-      value: 0.8888888888888888
+      value: 0.8818669971086328
     - name: F1
       type: f1
-      value: 0.8707262795872951
+      value: 0.8716064502959787
     - name: Accuracy
       type: accuracy
-      value: 0.9697812545270172
+      value: 0.9709691438504998
 ---
 
 <!-- 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. -->
 
 This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2032
-- Precision: 0.8533
-- Recall: 0.8889
-- F1: 0.8707
-- Accuracy: 0.9698
+- Loss: 0.1178
+- Precision: 0.8616
+- Recall: 0.8819
+- F1: 0.8716
+- Accuracy: 0.9710
 
 ## Model description
 
@@ -68,8 +68,8 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 1
-- eval_batch_size: 1
+- train_batch_size: 32
+- eval_batch_size: 32
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
@@ -77,13 +77,13 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1913        | 1.0   | 7193  | 0.1739          | 0.7382    | 0.8422 | 0.7868 | 0.9593   |
-| 0.1663        | 2.0   | 14386 | 0.1877          | 0.7835    | 0.8579 | 0.8190 | 0.9618   |
-| 0.1395        | 3.0   | 21579 | 0.1784          | 0.8391    | 0.8786 | 0.8584 | 0.9679   |
-| 0.0647        | 4.0   | 28772 | 0.1968          | 0.8314    | 0.8802 | 0.8551 | 0.9666   |
-| 0.0322        | 5.0   | 35965 | 0.2032          | 0.8533    | 0.8889 | 0.8707 | 0.9698   |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 225  | 0.1357          | 0.7953    | 0.8315 | 0.8130 | 0.9620   |
+| No log        | 2.0   | 450  | 0.1056          | 0.8245    | 0.8691 | 0.8462 | 0.9687   |
+| 0.21          | 3.0   | 675  | 0.1064          | 0.8487    | 0.8831 | 0.8656 | 0.9698   |
+| 0.21          | 4.0   | 900  | 0.1198          | 0.8442    | 0.8839 | 0.8636 | 0.9704   |
+| 0.0589        | 5.0   | 1125 | 0.1178          | 0.8616    | 0.8819 | 0.8716 | 0.9710   |
 
 
 ### Framework versions

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e9b720788fa1a6cfc327e57561dcab3ade6d5424f534a26ea2a70e395bd8878a
+oid sha256:25e0a15de0b2ca86e557a92ba3be911c6e380b1e4412858bd033dd55f2da3620
 size 2235481556