End of training

README.md

@@ -2,26 +2,29 @@
 license: apache-2.0
 base_model: distilbert-base-uncased
 tags:
-- generated_from_keras_callback
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
-- name: Anumaftab/my_awesome_wnut_model
+- name: my_awesome_wnut_model
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
+<!-- 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. -->
 
-# Anumaftab/my_awesome_wnut_model
+# my_awesome_wnut_model
 
 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.1552
-- Validation Loss: 0.2652
-- Train Precision: 0.5425
-- Train Recall: 0.3971
-- Train F1: 0.4586
-- Train Accuracy: 0.9433
-- Epoch: 1
+- Loss: 0.2668
+- Precision: 0.6099
+- Recall: 0.3086
+- F1: 0.4098
+- Accuracy: 0.9416
 
 ## Model description
 
@@ -40,20 +43,24 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
-- training_precision: float32
+- learning_rate: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
 
 ### Training results
 
-| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
-|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
-| 0.3364     | 0.3071          | 0.3297          | 0.1447       | 0.2012   | 0.9299         | 0     |
-| 0.1552     | 0.2652          | 0.5425          | 0.3971       | 0.4586   | 0.9433         | 1     |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 213  | 0.2833          | 0.5839    | 0.2419 | 0.3421 | 0.9385   |
+| No log        | 2.0   | 426  | 0.2668          | 0.6099    | 0.3086 | 0.4098 | 0.9416   |
 
 
 ### Framework versions
 
 - Transformers 4.35.2
-- TensorFlow 2.15.0
-- Datasets 2.16.1
+- Pytorch 2.1.0+cu121
 - Tokenizers 0.15.1