Training complete

README.md

@@ -2,22 +2,53 @@
 license: apache-2.0
 base_model: bert-base-cased
 tags:
-- generated_from_keras_callback
+- generated_from_trainer
+datasets:
+- conll2003
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
-- name: dsfdsf2/bert-finetuned-ner
-  results: []
+- name: bert-finetuned-ner
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: conll2003
+      type: conll2003
+      config: conll2003
+      split: validation
+      args: conll2003
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9325954072360813
+    - name: Recall
+      type: recall
+      value: 0.9500168293503871
+    - name: F1
+      type: f1
+      value: 0.9412255106294289
+    - name: Accuracy
+      type: accuracy
+      value: 0.9864308000235474
 ---
 
-<!-- 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. -->
 
-# dsfdsf2/bert-finetuned-ner
+# bert-finetuned-ner
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
+This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.0268
-- Validation Loss: 0.0516
-- Epoch: 2
+- Loss: 0.0618
+- Precision: 0.9326
+- Recall: 0.9500
+- F1: 0.9412
+- Accuracy: 0.9864
 
 ## Model description
 
@@ -36,21 +67,26 @@ 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': 2634, '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: mixed_float16
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
 
 ### Training results
 
-| Train Loss | Validation Loss | Epoch |
-|:----------:|:---------------:|:-----:|
-| 0.1686     | 0.0600          | 0     |
-| 0.0455     | 0.0551          | 1     |
-| 0.0268     | 0.0516          | 2     |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0739        | 1.0   | 1756 | 0.0703          | 0.8939    | 0.9275 | 0.9104 | 0.9800   |
+| 0.0365        | 2.0   | 3512 | 0.0627          | 0.9299    | 0.9468 | 0.9383 | 0.9857   |
+| 0.022         | 3.0   | 5268 | 0.0618          | 0.9326    | 0.9500 | 0.9412 | 0.9864   |
 
 
 ### Framework versions
 
 - Transformers 4.40.2
-- TensorFlow 2.15.0
+- Pytorch 2.2.1+cu121
 - Datasets 2.19.1
 - Tokenizers 0.19.1