Update README.md

README.md

@@ -49,34 +49,47 @@ It achieves the following results on the evaluation set (last epoch):
 - 'epoch': 4.0
 
 It achieves the following results on the test set:
--'eval_loss': 0.052769944071769714
--'eval_accuracy': 0.9933244325767691
--'eval_precision_per_label': [0.9956140350877193, 0.9923224568138196]
--'eval_recall_per_label': [0.9826839826839827, 0.9980694980694981]
--'eval_f1_per_label': [0.9891067538126361, 0.9951876804619827]
--'eval_precision_weighted': 0.9933376164683867
--'eval_recall_weighted': 0.9933244325767691
--'eval_f1_weighted': 0.993312254486016
+- 'eval_loss': 0.052769944071769714
+- 'eval_accuracy': 0.9933244325767691
+- 'eval_precision_per_label': [0.9956140350877193, 0.9923224568138196]
+- 'eval_recall_per_label': [0.9826839826839827, 0.9980694980694981]
+- 'eval_f1_per_label': [0.9891067538126361, 0.9951876804619827]
+- 'eval_precision_weighted': 0.9933376164683867
+- 'eval_recall_weighted': 0.9933244325767691
+- 'eval_f1_weighted': 0.993312254486016
 
 ## Training Details and Procedure
 
-Main Hyperparameters:
+## Main Hyperparameters:
+
+- evaluation_strategy: "epoch"
 - learning_rate: 1e-5
-- train_batch_size: 8
-- eval_batch_size: 18
-- optimizer: AdamW
-- lr_scheduler_type: linear
-- num_epochs: 4
+- per_device_train_batch_size: 8
+- per_device_eval_batch_size: 8
+- num_train_epochs: 4
+- weight_decay: 0.01
+- save_strategy: "epoch"
+- lr_scheduler_type: "linear"
+- warmup_steps: 449
+- logging_steps: 10
 
 
-#### Preprocessing and Postprocessing [optional]
+#### Preprocessing and Postprocessing:
 
--Needed to manually map dataset creating the different sets: train 60%, validation 20%, and test 20%
--Needed to manually map dataset's labels from str ("hateful", "non-hateful") to int (1,0), in order to properly create tensors.
--Dynamic Padding through DataCollator was used
+- Needed to manually map dataset creating the different sets: train 60%, validation 20%, and test 20%.
+- Needed to manually map dataset's labels from str ("hateful", "non-hateful") to int (1,0), in order to properly create tensors.
+- Dynamic Padding through DataCollator was used.
 
 
 ## More Information [optional]
 
-Fine-tuned by Javier de la Rosa Sánchez.
-[email protected]
+- Fine-tuned by Javier de la Rosa Sánchez.
+- [email protected]
+- https://www.linkedin.com/in/delarosajav95/
+
+### Framework versions
+
+- Transformers 4.47.0
+- Pytorch 2.5.1+cu121
+- Datasets 3.2.0
+- Tokenizers 0.21.0