Update README.md

README.md

@@ -5,9 +5,23 @@ tags:
 - generated_from_trainer
 metrics:
 - accuracy
+- f1
+- recall
+- precision
 model-index:
 - name: XLM_RoBERTa-Clickbait-Detection-new
   results: []
+datasets:
+- christinacdl/clickbait_detection_dataset
+language:
+- en
+- el
+- ru
+- ro
+- de
+- it
+- es
+pipeline_tag: text-classification
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -15,24 +29,27 @@ should probably proofread and complete it, then remove this comment. -->
 
 # XLM_RoBERTa-Clickbait-Detection-new
 
-This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
+This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the christinacdl/clickbait_detection_dataset dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.1071
 - Micro F1: 0.9834
 - Macro F1: 0.9833
 - Accuracy: 0.9834
 
-## Model description
+It achieves the following results on the test set:
+- Accuracy: 0.9838922630050172
+- Micro-F1 Score: 0.9838922630050172
+- Macro-F1 Score: 0.9838416247418498
+- Matthews Correlation Coefficient: 0.9676867009951606
 
-More information needed
+- Precision of each class: [0.98156425 0.98597897]
+- Recall of each class: [0.98431373 0.98351648]
+- F1 score of each class: [0.98293706 0.98474619]
 
 ## Intended uses & limitations
 
 More information needed
 
-## Training and evaluation data
-
-More information needed
 
 ## Training procedure
 
@@ -48,9 +65,47 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - num_epochs: 4
-
-### Training results
-
+- early stopping patience: 2
+- adam epsilon: 1e-8
+- gradient_checkpointing: True
+- max_grad_norm: 1.0
+- seed: 42
+- optimizer: adamw_torch_fused
+- max_steps': -1
+- warmup_ratio: 0
+- group_by_length: True
+- max_seq_length': 512
+- save_steps: 1000                
+- logging_steps': 500
+- evaluation_strategy: epoch
+- save_strategy:epoch
+- eval_steps: 1000
+- save_total_limit: 2
+
+
+### All results from Training and Evaluation
+- "epoch": 4.0,
+- "eval_accuracy": 0.9844203855294428,
+- "eval_loss": 0.08027808368206024,
+- "eval_macro_f1": 0.9843695357857132,
+- "eval_micro_f1": 0.9844203855294428,
+- "eval_runtime": 124.9733,
+- "eval_samples": 3787,
+- "eval_samples_per_second": 30.302,
+- "eval_steps_per_second": 1.896,
+- "predict_accuracy": 0.9838922630050172,
+- "predict_loss": 0.07716809958219528,
+- "predict_macro_f1": 0.9838416247418498,
+- "predict_micro_f1": 0.9838922630050172,
+- "predict_runtime": 127.7861,
+- "predict_samples": 3787,
+- "predict_samples_per_second": 29.635,
+- "predict_steps_per_second": 1.855,
+- "train_loss": 0.057462599486458765,
+- "train_runtime": 25253.576,
+- "train_samples": 30296,
+- "train_samples_per_second": 4.799,
+- "train_steps_per_second": 0.15
 
 
 ### Framework versions
@@ -58,4 +113,4 @@ The following hyperparameters were used during training:
 - Transformers 4.36.1
 - Pytorch 2.1.0+cu121
 - Datasets 2.13.1
-- Tokenizers 0.15.0
+- Tokenizers 0.15.0