Training in progress epoch 1

README.md

@@ -15,7 +15,7 @@ probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 12.5328
+- Train Loss: 12.0507
 - Validation Loss: 11.7955
 - Train Accuracy: 0.7647
 - Train Precision: [0.         0.76470588]
@@ -24,7 +24,7 @@ It achieves the following results on the evaluation set:
 - Train Recall W: 0.7647
 - Train F1: [0.         0.86666667]
 - Train F1 W: 0.6627
-- Epoch: 10
+- Epoch: 1
 
 ## Model description
 
@@ -43,23 +43,14 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -460, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 500, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
+- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 35, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 5, 'power': 1.0, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
 - training_precision: float32
 
 ### Training results
 
 | Train Loss | Validation Loss | Train Accuracy | Train Precision         | Train Precision W | Train Recall | Train Recall W | Train F1                | Train F1 W | Epoch |
 |:----------:|:---------------:|:--------------:|:-----------------------:|:-----------------:|:------------:|:--------------:|:-----------------------:|:----------:|:-----:|
-| 12.2918    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 1     |
-| 12.2918    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 2     |
-| 12.2918    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 3     |
-| 12.2918    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 4     |
-| 12.5328    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 5     |
-| 12.0507    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 6     |
-| 12.2918    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 7     |
-| 12.0507    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 8     |
-| 12.2918    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 9     |
-| 12.5328    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 10    |
+| 12.0507    | 11.7955         | 0.7647         | [0.         0.76470588] | 0.5848            | [0. 1.]      | 0.7647         | [0.         0.86666667] | 0.6627     | 1     |
 
 
 ### Framework versions