End of training

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_keras_callback
+model-index:
+- name: ratish/DBERT_MAKE_NewData_v1
+  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. -->
+
+# ratish/DBERT_MAKE_NewData_v1
+
+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:
+- Train Loss: 0.5238
+- Validation Loss: 0.6256
+- Train Accuracy: 0.8909
+- Epoch: 9
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 240, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
+- training_precision: float32
+
+### Training results
+
+| Train Loss | Validation Loss | Train Accuracy | Epoch |
+|:----------:|:---------------:|:--------------:|:-----:|
+| 2.2333     | 1.9085          | 0.6545         | 0     |
+| 1.6567     | 1.3839          | 0.6727         | 1     |
+| 1.2308     | 1.0679          | 0.8364         | 2     |
+| 0.9605     | 0.8879          | 0.8364         | 3     |
+| 0.8155     | 0.7807          | 0.8364         | 4     |
+| 0.7106     | 0.7242          | 0.8545         | 5     |
+| 0.6365     | 0.6794          | 0.8182         | 6     |
+| 0.5894     | 0.6334          | 0.8909         | 7     |
+| 0.5446     | 0.6293          | 0.8909         | 8     |
+| 0.5238     | 0.6256          | 0.8909         | 9     |
+
+
+### Framework versions
+
+- Transformers 4.28.1
+- TensorFlow 2.12.0
+- Datasets 2.12.0
+- Tokenizers 0.13.3

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "id2label": {
+    "0": "hyundai",
+    "1": "jaguar",
+    "2": "chevrolet",
+    "3": "lincoln",
+    "4": "ford",
+    "5": "tesla",
+    "6": "lexus",
+    "7": "chrysler",
+    "8": "nissan",
+    "9": "toyota",
+    "10": "mercedes-benz",
+    "11": "autonomous",
+    "12": "cruise"
+  },
+  "initializer_range": 0.02,
+  "label2id": {
+    "autonomous": 11,
+    "chevrolet": 2,
+    "chrysler": 7,
+    "cruise": 12,
+    "ford": 4,
+    "hyundai": 0,
+    "jaguar": 1,
+    "lexus": 6,
+    "lincoln": 3,
+    "mercedes-benz": 10,
+    "nissan": 8,
+    "tesla": 5,
+    "toyota": 9
+  },
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.28.1",
+  "vocab_size": 30522
+}

special_tokens_map.json

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

tf_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f0e3a05fd183d3ca086198373afca43f9c38168600ab39c18a133a001083af1c
+size 267985600

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}