End of training

README.md

@@ -1,5 +1,6 @@
 ---
-base_model: finiteautomata/beto-sentiment-analysis
+license: apache-2.0
+base_model: google/electra-base-discriminator
 tags:
 - generated_from_trainer
 metrics:
@@ -7,23 +8,23 @@ metrics:
 - precision
 - recall
 model-index:
-- name: beto-sentiment-analysis-finetuned-detests-wandb24
+- name: electra-base-discriminator-finetuned-detests-wandb24
   results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# beto-sentiment-analysis-finetuned-detests-wandb24
+# electra-base-discriminator-finetuned-detests-wandb24
 
-This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on the None dataset.
+This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.6204
-- Accuracy: 0.8674
-- F1-score: 0.7993
-- Precision: 0.8225
-- Recall: 0.7822
-- Auc: 0.7822
+- Loss: 0.4457
+- Accuracy: 0.7741
+- F1-score: 0.6965
+- Precision: 0.6879
+- Recall: 0.7092
+- Auc: 0.7092
 
 ## Model description
 
@@ -48,16 +49,14 @@ 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: 4
+- num_epochs: 2
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc    |
 |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
-| 0.393         | 1.0   | 77   | 0.3365          | 0.8592   | 0.7633   | 0.8424    | 0.7287 | 0.7287 |
-| 0.1947        | 2.0   | 154  | 0.3843          | 0.8396   | 0.7845   | 0.7716    | 0.8023 | 0.8023 |
-| 0.0597        | 3.0   | 231  | 0.5486          | 0.8740   | 0.8046   | 0.8398    | 0.7814 | 0.7814 |
-| 0.0028        | 4.0   | 308  | 0.6204          | 0.8674   | 0.7993   | 0.8225    | 0.7822 | 0.7822 |
+| 0.4719        | 1.0   | 77   | 0.4698          | 0.7971   | 0.6380   | 0.7159    | 0.6199 | 0.6199 |
+| 0.4737        | 2.0   | 154  | 0.4457          | 0.7741   | 0.6965   | 0.6879    | 0.7092 | 0.7092 |
 
 
 ### Framework versions