Training complete

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: bert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: bert-ia-checkpoint
+  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-ia-checkpoint
+
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.7216
+- Accuracy: 0.7229
+- F1 Macro: 0.6963
+- Precision Macro: 0.7200
+- Recall Macro: 0.6916
+- Auc: 0.7626
+
+## 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: 2e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- 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: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Precision Macro | Recall Macro | Auc    |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:------:|
+| No log        | 1.0   | 79   | 0.6736          | 0.7261   | 0.7028   | 0.7210          | 0.6981       | 0.7428 |
+| No log        | 2.0   | 158  | 0.8024          | 0.7006   | 0.6975   | 0.6995          | 0.7070       | 0.7566 |
+| No log        | 3.0   | 237  | 0.9896          | 0.7389   | 0.7226   | 0.7307          | 0.7189       | 0.7613 |
+| No log        | 4.0   | 316  | 1.3463          | 0.7229   | 0.7032   | 0.7145          | 0.6992       | 0.7444 |
+| No log        | 5.0   | 395  | 1.4706          | 0.7357   | 0.7246   | 0.7256          | 0.7238       | 0.7536 |
+| No log        | 6.0   | 474  | 1.6432          | 0.7420   | 0.7264   | 0.7339          | 0.7228       | 0.7518 |
+| 0.176         | 7.0   | 553  | 1.7216          | 0.7229   | 0.6963   | 0.7200          | 0.6916       | 0.7626 |
+| 0.176         | 8.0   | 632  | 1.7837          | 0.7357   | 0.7078   | 0.7383          | 0.7023       | 0.7596 |
+| 0.176         | 9.0   | 711  | 1.7627          | 0.7325   | 0.7129   | 0.7256          | 0.7085       | 0.7611 |
+| 0.176         | 10.0  | 790  | 1.7560          | 0.7357   | 0.7188   | 0.7275          | 0.7149       | 0.7610 |
+
+
+### Framework versions
+
+- Transformers 4.57.1
+- Pytorch 2.8.0+cu126
+- Datasets 4.0.0
+- Tokenizers 0.22.1

config.json

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+{
+  "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": 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",
+  "transformers_version": "4.57.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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+oid sha256:d5d1b78bcfa02438c479c75b68513c32ac7bf947c66dd349f066e2001092289e
+size 437958648

training_args.bin

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