End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google-bert/bert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: bert-wellness-classifier
+  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. -->
+
+# bert-wellness-classifier
+
+This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7555
+- Accuracy: 0.714
+- Auc: 0.894
+- Precision Class 0: 0.771
+- Precision Class 1: 0.789
+- Precision Class 2: 0.78
+- Precision Class 3: 0.653
+- Recall Class 0: 0.698
+- Recall Class 1: 0.556
+- Recall Class 2: 0.619
+- Recall Class 3: 0.827
+
+## 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:
+- learning_rate: 0.0002
+- 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc   | Precision Class 0 | Precision Class 1 | Precision Class 2 | Precision Class 3 | Recall Class 0 | Recall Class 1 | Recall Class 2 | Recall Class 3 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|:-----------------:|:-----------------:|:-----------------:|:-----------------:|:--------------:|:--------------:|:--------------:|:--------------:|
+| 1.1543        | 1.0   | 140  | 1.0293          | 0.568    | 0.835 | 0.767             | 1.0               | 0.447             | 0.587             | 0.434          | 0.296          | 0.667          | 0.653          |
+| 0.9519        | 2.0   | 280  | 0.9149          | 0.585    | 0.86  | 0.738             | 1.0               | 0.613             | 0.528             | 0.585          | 0.185          | 0.302          | 0.878          |
+| 0.8294        | 3.0   | 420  | 0.7950          | 0.676    | 0.88  | 0.74              | 0.824             | 0.606             | 0.67              | 0.698          | 0.519          | 0.683          | 0.704          |
+| 0.7821        | 4.0   | 560  | 0.8809          | 0.598    | 0.882 | 0.87              | 0.727             | 0.604             | 0.545             | 0.377          | 0.296          | 0.508          | 0.857          |
+| 0.7349        | 5.0   | 700  | 0.7576          | 0.701    | 0.892 | 0.809             | 0.789             | 0.714             | 0.639             | 0.717          | 0.556          | 0.635          | 0.776          |
+| 0.7173        | 6.0   | 840  | 0.7700          | 0.689    | 0.89  | 0.76              | 0.824             | 0.744             | 0.626             | 0.717          | 0.519          | 0.508          | 0.837          |
+| 0.7048        | 7.0   | 980  | 0.7977          | 0.68     | 0.892 | 0.822             | 0.842             | 0.793             | 0.595             | 0.698          | 0.593          | 0.365          | 0.898          |
+| 0.6776        | 8.0   | 1120 | 0.7654          | 0.705    | 0.892 | 0.745             | 0.8               | 0.778             | 0.648             | 0.717          | 0.593          | 0.556          | 0.827          |
+| 0.6649        | 9.0   | 1260 | 0.7612          | 0.718    | 0.895 | 0.837             | 0.789             | 0.78              | 0.643             | 0.679          | 0.556          | 0.619          | 0.847          |
+| 0.676         | 10.0  | 1400 | 0.7555          | 0.714    | 0.894 | 0.771             | 0.789             | 0.78              | 0.653             | 0.698          | 0.556          | 0.619          | 0.827          |
+
+
+### Framework versions
+
+- Transformers 4.45.1
+- Pytorch 2.4.0+cpu
+- Datasets 3.0.1
+- Tokenizers 0.20.0

config.json

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+{
+  "_name_or_path": "google-bert/bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "PA",
+    "1": "IVA",
+    "2": "SA",
+    "3": "SEA"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "IVA": 1,
+    "PA": 0,
+    "SA": 2,
+    "SEA": 3
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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runs/Nov03_07-43-26_dcc4d17321e8/events.out.tfevents.1730619815.dcc4d17321e8.30.0

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special_tokens_map.json

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

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
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+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  },
+  "clean_up_tokenization_spaces": false,
+  "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": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_args.bin

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