sianbrumm/class_classificator_ndc

Browse files

Files changed (4) hide show

README.md +12 -11
config.json +134 -134
model.safetensors +1 -1
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.0045
-- Precision: 0.9378
-- Recall: 0.9378
-- F1: 0.9378
-- Accuracy: 0.9378
 ## Model description
@@ -50,17 +50,18 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- num_epochs: 5
 ### Training results
 | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.1623        | 1.0   | 2527  | 0.9948          | 0.9213    | 0.9213 | 0.9213 | 0.9213   |
-| 0.1321        | 2.0   | 5054  | 0.9662          | 0.9268    | 0.9268 | 0.9268 | 0.9268   |
-| 0.063         | 3.0   | 7581  | 0.9424          | 0.9330    | 0.9330 | 0.9330 | 0.9330   |
-| 0.0247        | 4.0   | 10108 | 0.9948          | 0.9351    | 0.9351 | 0.9351 | 0.9351   |
-| 0.007         | 5.0   | 12635 | 1.0045          | 0.9378    | 0.9378 | 0.9378 | 0.9378   |
 ### Framework versions

 This model is a fine-tuned version of [dbmdz/bert-base-german-uncased](https://huggingface.co/dbmdz/bert-base-german-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.7191
+- Precision: 0.9280
+- Recall: 0.9280
+- F1: 0.9280
+- Accuracy: 0.9280
 ## Model description
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
+- num_epochs: 6
 ### Training results
 | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 1.3487        | 1.0   | 2527  | 1.1844          | 0.8355    | 0.8355 | 0.8355 | 0.8355   |
+| 0.9081        | 2.0   | 5054  | 0.9115          | 0.8897    | 0.8897 | 0.8897 | 0.8897   |
+| 0.6327        | 3.0   | 7581  | 0.7873          | 0.9038    | 0.9038 | 0.9038 | 0.9038   |
+| 0.3874        | 4.0   | 10108 | 0.7599          | 0.9196    | 0.9196 | 0.9196 | 0.9196   |
+| 0.2643        | 5.0   | 12635 | 0.7191          | 0.9280    | 0.9280 | 0.9280 | 0.9280   |
+| 0.2146        | 6.0   | 15162 | 0.7315          | 0.9300    | 0.9300 | 0.9300 | 0.9300   |
 ### Framework versions

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