End of training

README.md

@@ -2,26 +2,53 @@
 license: apache-2.0
 base_model: distilbert-base-uncased
 tags:
-- generated_from_keras_callback
+- generated_from_trainer
+datasets:
+- wnut_17
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
-- name: Mohamedfasil/my_awesome_wnut_model
-  results: []
+- name: my_awesome_wnut_model
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: wnut_17
+      type: wnut_17
+      config: wnut_17
+      split: test
+      args: wnut_17
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.578853046594982
+    - name: Recall
+      type: recall
+      value: 0.29935125115848005
+    - name: F1
+      type: f1
+      value: 0.3946243127672571
+    - name: Accuracy
+      type: accuracy
+      value: 0.9410884528237357
 ---
 
-<!-- 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. -->
 
-# Mohamedfasil/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.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.1100
-- Validation Loss: 0.2607
-- Train Precision: 0.5710
-- Train Recall: 0.4378
-- Train F1: 0.4956
-- Train Accuracy: 0.9469
-- Epoch: 2
+- Loss: 0.2727
+- Precision: 0.5789
+- Recall: 0.2994
+- F1: 0.3946
+- Accuracy: 0.9411
 
 ## Model description
 
@@ -40,21 +67,25 @@ 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 |
-|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
-| 0.1100     | 0.2607          | 0.5710          | 0.4378       | 0.4956   | 0.9469         | 0     |
-| 0.1101     | 0.2607          | 0.5710          | 0.4378       | 0.4956   | 0.9469         | 1     |
-| 0.1100     | 0.2607          | 0.5710          | 0.4378       | 0.4956   | 0.9469         | 2     |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| No log        | 1.0   | 213  | 0.2873          | 0.5128    | 0.2234 | 0.3112 | 0.9376   |
+| No log        | 2.0   | 426  | 0.2727          | 0.5789    | 0.2994 | 0.3946 | 0.9411   |
 
 
 ### Framework versions
 
 - Transformers 4.35.2
-- TensorFlow 2.14.0
+- Pytorch 2.1.0+cu118
 - Datasets 2.15.0
 - Tokenizers 0.15.0