has-abi
/

bert-finetuned-resumes-sections

@@ -17,10 +17,10 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.0272
-- F1: 0.9625
-- Roc Auc: 0.9793
-- Accuracy: 0.9612
 ## Model description
@@ -45,22 +45,24 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 10
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
-| 0.1714        | 1.0   | 554  | 0.0653          | 0.9301 | 0.9515  | 0.9061   |
-| 0.0554        | 2.0   | 1108 | 0.0392          | 0.9489 | 0.9679  | 0.9395   |
-| 0.033         | 3.0   | 1662 | 0.0318          | 0.9564 | 0.9743  | 0.9512   |
-| 0.0212        | 4.0   | 2216 | 0.0295          | 0.9574 | 0.9748  | 0.9530   |
-| 0.0155        | 5.0   | 2770 | 0.0282          | 0.9587 | 0.9757  | 0.9548   |
-| 0.0138        | 6.0   | 3324 | 0.0282          | 0.9615 | 0.9776  | 0.9584   |
-| 0.0108        | 7.0   | 3878 | 0.0272          | 0.9625 | 0.9793  | 0.9612   |
-| 0.0081        | 8.0   | 4432 | 0.0284          | 0.9597 | 0.9775  | 0.9584   |
-| 0.0077        | 9.0   | 4986 | 0.0267          | 0.9602 | 0.9779  | 0.9584   |
-| 0.0058        | 10.0  | 5540 | 0.0281          | 0.9579 | 0.9765  | 0.9566   |
 ### Framework versions

 This model is a fine-tuned version of [dbmdz/bert-base-french-europeana-cased](https://huggingface.co/dbmdz/bert-base-french-europeana-cased) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.0333
+- F1: 0.9548
+- Roc Auc: 0.9732
+- Accuracy: 0.9493
 ## Model description
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 12
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
+| 0.1659        | 1.0   | 601  | 0.0645          | 0.9201 | 0.9434  | 0.8910   |
+| 0.055         | 2.0   | 1202 | 0.0426          | 0.9407 | 0.9633  | 0.9309   |
+| 0.0324        | 3.0   | 1803 | 0.0371          | 0.9450 | 0.9663  | 0.9368   |
+| 0.0226        | 4.0   | 2404 | 0.0389          | 0.9402 | 0.9651  | 0.9343   |
+| 0.0125        | 5.0   | 3005 | 0.0354          | 0.9433 | 0.9650  | 0.9343   |
+| 0.0091        | 6.0   | 3606 | 0.0364          | 0.9482 | 0.9696  | 0.9434   |
+| 0.0075        | 7.0   | 4207 | 0.0363          | 0.9464 | 0.9676  | 0.9393   |
+| 0.007         | 8.0   | 4808 | 0.0333          | 0.9548 | 0.9732  | 0.9493   |
+| 0.0063        | 9.0   | 5409 | 0.0358          | 0.9501 | 0.9698  | 0.9434   |
+| 0.0043        | 10.0  | 6010 | 0.0380          | 0.9475 | 0.9707  | 0.9443   |
+| 0.0032        | 11.0  | 6611 | 0.0377          | 0.9491 | 0.9712  | 0.9468   |
+| 0.0031        | 12.0  | 7212 | 0.0375          | 0.9500 | 0.9716  | 0.9459   |
 ### Framework versions