Training in progress epoch 0

README.md

@@ -1,36 +1,23 @@
 ---
 license: apache-2.0
 tags:
-- generated_from_trainer
-datasets:
-- glue
-metrics:
-- accuracy
+- generated_from_keras_callback
 model-index:
-- name: distilbert-base-uncased-finetuned-sst2
-  results:
-  - task:
-      name: Text Classification
-      type: text-classification
-    dataset:
-      name: glue
-      type: glue
-      args: sst2
-    metrics:
-    - name: Accuracy
-      type: accuracy
-      value: 0.908256880733945
+- name: rwang5688/distilbert-base-uncased-finetuned-sst2
+  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. -->
 
-# distilbert-base-uncased-finetuned-sst2
+# rwang5688/distilbert-base-uncased-finetuned-sst2
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4101
-- Accuracy: 0.9083
+- Train Loss: 0.2133
+- Validation Loss: 0.2615
+- Train Accuracy: 0.9083
+- Epoch: 0
 
 ## Model description
 
@@ -49,28 +36,19 @@ More information needed
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
-- 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: 5
+- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 21045, '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
 
-| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
-|:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 0.1843        | 1.0   | 4210  | 0.3040          | 0.9037   |
-| 0.1253        | 2.0   | 8420  | 0.3577          | 0.8968   |
-| 0.0818        | 3.0   | 12630 | 0.4101          | 0.9083   |
-| 0.0676        | 4.0   | 16840 | 0.4326          | 0.8991   |
-| 0.0455        | 5.0   | 21050 | 0.5258          | 0.9002   |
+| Train Loss | Validation Loss | Train Accuracy | Epoch |
+|:----------:|:---------------:|:--------------:|:-----:|
+| 0.2133     | 0.2615          | 0.9083         | 0     |
 
 
 ### Framework versions
 
-- Transformers 4.12.0
-- Pytorch 1.8.1+cpu
+- Transformers 4.21.1
+- TensorFlow 2.9.1
 - Datasets 2.4.0
-- Tokenizers 0.10.3
+- Tokenizers 0.12.1

config.json

@@ -14,12 +14,10 @@
   "n_heads": 12,
   "n_layers": 6,
   "pad_token_id": 0,
-  "problem_type": "single_label_classification",
   "qa_dropout": 0.1,
   "seq_classif_dropout": 0.2,
   "sinusoidal_pos_embds": false,
   "tie_weights_": true,
-  "torch_dtype": "float32",
-  "transformers_version": "4.12.0",
+  "transformers_version": "4.21.1",
   "vocab_size": 30522
 }

logs/train/events.out.tfevents.1659891609.datascience-1-0-ml-g4dn-16xlarge-0857614900e4015bb2bd851cec87.13488.0.v2

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logs/validation/events.out.tfevents.1659892589.datascience-1-0-ml-g4dn-16xlarge-0857614900e4015bb2bd851cec87.13488.1.v2

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special_tokens_map.json

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-{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tf_model.h5

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tokenizer_config.json

@@ -1 +1,14 @@
-{"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "distilbert-base-uncased", "tokenizer_class": "DistilBertTokenizer"}
+{
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "distilbert-base-uncased",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}