Training in progress epoch 0

README.md

@@ -15,17 +15,17 @@ probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Train Loss: 0.0650
-- Train Accuracy: 0.9781
-- Train F1 M: 0.5639
-- Train Precision M: 0.4054
-- Train Recall M: 0.9861
-- Validation Loss: 0.2272
-- Validation Accuracy: 0.9268
-- Validation F1 M: 0.5645
-- Validation Precision M: 0.4042
-- Validation Recall M: 0.9815
-- Epoch: 3
+- Train Loss: 0.6856
+- Train Accuracy: 0.5783
+- Train F1 M: 0.0409
+- Train Precision M: 0.0564
+- Train Recall M: 0.0362
+- Validation Loss: 0.6755
+- Validation Accuracy: 0.5937
+- Validation F1 M: 0.0
+- Validation Precision M: 0.0
+- Validation Recall M: 0.0
+- Epoch: 0
 
 ## Model description
 
@@ -44,22 +44,19 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3032, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
+- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 36384, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
 - training_precision: float32
 
 ### Training results
 
 | Train Loss | Train Accuracy | Train F1 M | Train Precision M | Train Recall M | Validation Loss | Validation Accuracy | Validation F1 M | Validation Precision M | Validation Recall M | Epoch |
 |:----------:|:--------------:|:----------:|:-----------------:|:--------------:|:---------------:|:-------------------:|:---------------:|:----------------------:|:-------------------:|:-----:|
-| 0.3612     | 0.8415         | 0.5169     | 0.3925            | 0.8303         | 0.2233          | 0.9169              | 0.5623          | 0.4054                 | 0.9621              | 0     |
-| 0.1787     | 0.9354         | 0.5550     | 0.4025            | 0.9562         | 0.2166          | 0.9182              | 0.5720          | 0.4110                 | 0.9869              | 1     |
-| 0.1092     | 0.9599         | 0.5601     | 0.4046            | 0.9746         | 0.2161          | 0.9202              | 0.5650          | 0.4054                 | 0.9763              | 2     |
-| 0.0650     | 0.9781         | 0.5639     | 0.4054            | 0.9861         | 0.2272          | 0.9268              | 0.5645          | 0.4042                 | 0.9815              | 3     |
+| 0.6856     | 0.5783         | 0.0409     | 0.0564            | 0.0362         | 0.6755          | 0.5937              | 0.0             | 0.0                    | 0.0                 | 0     |
 
 
 ### Framework versions
 
 - Transformers 4.34.1
-- TensorFlow 2.14.0
+- TensorFlow 2.10.0
 - Datasets 2.14.5
 - Tokenizers 0.14.1

tf_model.h5

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