trainer: training complete at 2023-11-14 13:31:47.312072.

README.md

@@ -3,6 +3,8 @@ license: apache-2.0
 base_model: distilbert-base-uncased
 tags:
 - generated_from_trainer
+metrics:
+- accuracy
 model-index:
 - name: bert-ner-essays-classify_span
   results: []
@@ -16,13 +18,12 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.6951
-- Ajorclaim: {'precision': 0.5098039215686274, 'recall': 0.4, 'f1': 0.4482758620689655, 'number': 65}
-- Laim: {'precision': 0.29545454545454547, 'recall': 0.23008849557522124, 'f1': 0.2587064676616916, 'number': 113}
-- Remise: {'precision': 0.23140495867768596, 'recall': 0.20588235294117646, 'f1': 0.2178988326848249, 'number': 136}
-- Overall Precision: 0.3077
-- Overall Recall: 0.2548
-- Overall F1: 0.2787
-- Overall Accuracy: 0.7077
+- Claim: {'precision': 0.4811320754716981, 'recall': 0.3541666666666667, 'f1-score': 0.408, 'support': 144.0}
+- Majorclaim: {'precision': 0.625, 'recall': 0.4861111111111111, 'f1-score': 0.5468749999999999, 'support': 72.0}
+- Premise: {'precision': 0.7718120805369127, 'recall': 0.8778625954198473, 'f1-score': 0.8214285714285714, 'support': 393.0}
+- Accuracy: 0.7077
+- Macro avg: {'precision': 0.6259813853362036, 'recall': 0.5727134577325418, 'f1-score': 0.5921011904761905, 'support': 609.0}
+- Weighted avg: {'precision': 0.6857227693250102, 'recall': 0.7077175697865353, 'f1-score': 0.6912125263898662, 'support': 609.0}
 
 ## Model description
 
@@ -51,10 +52,10 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Ajorclaim                                                                                | Laim                                                                                                        | Remise                                                                                                     | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:----------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
-| No log        | 1.0   | 267  | 0.7245          | {'precision': 0.5, 'recall': 0.2153846153846154, 'f1': 0.3010752688172043, 'number': 65} | {'precision': 0.1794871794871795, 'recall': 0.061946902654867256, 'f1': 0.09210526315789473, 'number': 113} | {'precision': 0.15625, 'recall': 0.07352941176470588, 'f1': 0.1, 'number': 136}                            | 0.2366            | 0.0987         | 0.1393     | 0.6650           |
-| 0.7275        | 2.0   | 534  | 0.6951          | {'precision': 0.5098039215686274, 'recall': 0.4, 'f1': 0.4482758620689655, 'number': 65} | {'precision': 0.29545454545454547, 'recall': 0.23008849557522124, 'f1': 0.2587064676616916, 'number': 113}  | {'precision': 0.23140495867768596, 'recall': 0.20588235294117646, 'f1': 0.2178988326848249, 'number': 136} | 0.3077            | 0.2548         | 0.2787     | 0.7077           |
+| Training Loss | Epoch | Step | Validation Loss | Claim                                                                                                                | Majorclaim                                                                                          | Premise                                                                                                           | Accuracy | Macro avg                                                                                                         | Weighted avg                                                                                                      |
+|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|:--------:|:-----------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------:|
+| No log        | 1.0   | 267  | 0.7245          | {'precision': 0.35714285714285715, 'recall': 0.10416666666666667, 'f1-score': 0.16129032258064516, 'support': 144.0} | {'precision': 0.5806451612903226, 'recall': 0.25, 'f1-score': 0.34951456310679613, 'support': 72.0} | {'precision': 0.6940298507462687, 'recall': 0.9465648854961832, 'f1-score': 0.8008611410118407, 'support': 393.0} | 0.6650   | {'precision': 0.5439392897264828, 'recall': 0.4335771840542833, 'f1-score': 0.4372220088997607, 'support': 609.0} | {'precision': 0.6009667559684043, 'recall': 0.6650246305418719, 'f1-score': 0.5962714013349024, 'support': 609.0} |
+| 0.7275        | 2.0   | 534  | 0.6951          | {'precision': 0.4811320754716981, 'recall': 0.3541666666666667, 'f1-score': 0.408, 'support': 144.0}                 | {'precision': 0.625, 'recall': 0.4861111111111111, 'f1-score': 0.5468749999999999, 'support': 72.0} | {'precision': 0.7718120805369127, 'recall': 0.8778625954198473, 'f1-score': 0.8214285714285714, 'support': 393.0} | 0.7077   | {'precision': 0.6259813853362036, 'recall': 0.5727134577325418, 'f1-score': 0.5921011904761905, 'support': 609.0} | {'precision': 0.6857227693250102, 'recall': 0.7077175697865353, 'f1-score': 0.6912125263898662, 'support': 609.0} |
 
 
 ### Framework versions