Training complete

README.md

@@ -2,22 +2,29 @@
 license: apache-2.0
 base_model: bert-base-cased
 tags:
-- generated_from_keras_callback
+- generated_from_trainer
+metrics:
+- precision
+- recall
+- f1
+- accuracy
 model-index:
-- name: tr-aravindan/bert-finetuned-ner
+- name: bert-finetuned-ner
   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. -->
 
-# tr-aravindan/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.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.0266
-- Validation Loss: 0.0531
-- Epoch: 2
+- Loss: 0.0585
+- Precision: 0.9362
+- Recall: 0.9509
+- F1: 0.9435
+- Accuracy: 0.9864
 
 ## Model description
 
@@ -36,21 +43,26 @@ 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 |
-|:----------:|:---------------:|:-----:|
-| 0.1673     | 0.0670          | 0     |
-| 0.0460     | 0.0563          | 1     |
-| 0.0266     | 0.0531          | 2     |
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.0799        | 1.0   | 1756 | 0.0809          | 0.9092    | 0.9332 | 0.9210 | 0.9789   |
+| 0.0402        | 2.0   | 3512 | 0.0574          | 0.9263    | 0.9473 | 0.9367 | 0.9858   |
+| 0.0255        | 3.0   | 5268 | 0.0585          | 0.9362    | 0.9509 | 0.9435 | 0.9864   |
 
 
 ### Framework versions
 
-- Transformers 4.35.0
-- TensorFlow 2.14.0
-- Datasets 2.14.6
-- Tokenizers 0.14.1
+- Transformers 4.37.2
+- Pytorch 2.1.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1