Training in progress epoch 0

README.md

@@ -1,53 +1,22 @@
 ---
 license: apache-2.0
 tags:
-- generated_from_trainer
-datasets:
-- conll2003
-metrics:
-- precision
-- recall
-- f1
-- accuracy
+- generated_from_keras_callback
 model-index:
-- 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: 0.9363531162175566
-    - name: Recall
-      type: recall
-      value: 0.9532144059239314
-    - name: F1
-      type: f1
-      value: 0.9447085313985489
-    - name: Accuracy
-      type: accuracy
-      value: 0.9864308000235474
+- name: fadliaulawi/bert-finetuned-ner
+  results: []
 ---
 
-<!-- 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. -->
+<!-- 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. -->
 
-# bert-finetuned-ner
+# fadliaulawi/bert-finetuned-ner
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
+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:
-- Loss: 0.0635
-- Precision: 0.9364
-- Recall: 0.9532
-- F1: 0.9447
-- Accuracy: 0.9864
+- Train Loss: 0.1753
+- Validation Loss: 0.0690
+- Epoch: 0
 
 ## Model description
 
@@ -66,26 +35,19 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- 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
+- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2634, '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, 'weight_decay_rate': 0.01}
+- training_precision: mixed_float16
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
-| 0.0879        | 1.0   | 1756 | 0.0723          | 0.9264    | 0.9382 | 0.9323 | 0.9817   |
-| 0.0337        | 2.0   | 3512 | 0.0637          | 0.9298    | 0.9492 | 0.9394 | 0.9856   |
-| 0.0175        | 3.0   | 5268 | 0.0635          | 0.9364    | 0.9532 | 0.9447 | 0.9864   |
+| Train Loss | Validation Loss | Epoch |
+|:----------:|:---------------:|:-----:|
+| 0.1753     | 0.0690          | 0     |
 
 
 ### Framework versions
 
 - Transformers 4.30.2
-- Pytorch 2.0.1+cu118
+- TensorFlow 2.12.0
 - Datasets 2.13.1
 - Tokenizers 0.13.3

config.json

@@ -40,7 +40,6 @@
   "num_hidden_layers": 12,
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
-  "torch_dtype": "float32",
   "transformers_version": "4.30.2",
   "type_vocab_size": 2,
   "use_cache": true,

tf_model.h5

@@ -0,0 +1,3 @@
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+oid sha256:990f9b93adb4b41990d4cf0bcde88f596e26ddb7d89e8718858137f4b851bdda
+size 431179756