Model save

README.md

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+---
+license: apache-2.0
+base_model: bert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+model-index:
+- name: email_spam_classification_eng
+  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. -->
+
+# email_spam_classification_eng
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1368
+- Accuracy: 0.9880
+- Precision: 0.9880
+- Recall: 0.9880
+- F1 Score: 0.9879
+
+## 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: 3e-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: 6
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
+| 0.0186        | 1.0   | 592  | 0.1796          | 0.9833   | 0.9833    | 0.9833 | 0.9829   |
+| 0.0078        | 2.0   | 1184 | 0.1189          | 0.9868   | 0.9868    | 0.9868 | 0.9867   |
+| 0.006         | 3.0   | 1776 | 0.1062          | 0.9868   | 0.9868    | 0.9868 | 0.9866   |
+| 0.0           | 4.0   | 2368 | 0.1319          | 0.9880   | 0.9880    | 0.9880 | 0.9879   |
+| 0.0           | 5.0   | 2960 | 0.1356          | 0.9880   | 0.9880    | 0.9880 | 0.9879   |
+| 0.0           | 6.0   | 3552 | 0.1368          | 0.9880   | 0.9880    | 0.9880 | 0.9879   |
+
+
+### Framework versions
+
+- Transformers 4.37.0
+- Pytorch 2.1.2
+- Datasets 2.1.0
+- Tokenizers 0.15.1

config.json

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+{
+  "_name_or_path": "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,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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": "4.37.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+size 437958648

training_args.bin

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