End of training

README.md

@@ -1,52 +1,52 @@
----
-library_name: transformers
-base_model: ibi15/spam-detection
-tags:
-- generated_from_trainer
-model-index:
-- name: spam-detection
-  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. -->
-
-# spam-detection
-
-This model is a fine-tuned version of [ibi15/spam-detection](https://huggingface.co/ibi15/spam-detection) on the None dataset.
-
-## 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: 5e-05
-- train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 3
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.57.5
-- Pytorch 2.9.1+cpu
-- Datasets 4.4.2
-- Tokenizers 0.22.2
+---
+library_name: transformers
+base_model: ibi15/spam-detection
+tags:
+- generated_from_trainer
+model-index:
+- name: Spam-Detection
+  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. -->
+
+# Spam-Detection
+
+This model is a fine-tuned version of [ibi15/spam-detection](https://huggingface.co/ibi15/spam-detection) on the None dataset.
+
+## 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: 5e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.57.5
+- Pytorch 2.9.1+cpu
+- Datasets 4.4.2
+- Tokenizers 0.22.2

config.json

@@ -1,26 +1,26 @@
-{
-  "architectures": [
-    "BertForSequenceClassification"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "dtype": "float32",
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 128,
-  "initializer_range": 0.02,
-  "intermediate_size": 512,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 2,
-  "num_hidden_layers": 2,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "problem_type": "single_label_classification",
-  "transformers_version": "4.57.5",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
-}
+{
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 128,
+  "initializer_range": 0.02,
+  "intermediate_size": 512,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 2,
+  "num_hidden_layers": 2,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "transformers_version": "4.57.5",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

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training_args.bin

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