End of training

README.md

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+---
+license: apache-2.0
+base_model: google-bert/bert-base-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+model-index:
+- name: hf-bert-finetuning
+  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. -->
+
+# hf-bert-finetuning
+
+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: 2.2672
+- Accuracy: 0.805
+
+## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 100
+
+### Training results
+
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|
+| 0.392         | 4.0   | 500   | 1.1767          | 0.805    |
+| 0.0421        | 8.0   | 1000  | 1.3555          | 0.814    |
+| 0.0266        | 12.0  | 1500  | 1.7734          | 0.806    |
+| 0.0066        | 16.0  | 2000  | 1.6149          | 0.818    |
+| 0.0264        | 20.0  | 2500  | 1.4583          | 0.823    |
+| 0.0284        | 24.0  | 3000  | 1.8117          | 0.794    |
+| 0.0019        | 28.0  | 3500  | 1.8569          | 0.804    |
+| 0.0336        | 32.0  | 4000  | 1.8200          | 0.801    |
+| 0.0221        | 36.0  | 4500  | 1.8082          | 0.806    |
+| 0.0195        | 40.0  | 5000  | 1.8102          | 0.81     |
+| 0.007         | 44.0  | 5500  | 1.9712          | 0.82     |
+| 0.0028        | 48.0  | 6000  | 1.8803          | 0.818    |
+| 0.0017        | 52.0  | 6500  | 1.9739          | 0.82     |
+| 0.0           | 56.0  | 7000  | 2.0171          | 0.821    |
+| 0.019         | 60.0  | 7500  | 1.9017          | 0.805    |
+| 0.0           | 64.0  | 8000  | 2.0914          | 0.801    |
+| 0.0           | 68.0  | 8500  | 2.1453          | 0.799    |
+| 0.0061        | 72.0  | 9000  | 2.2067          | 0.786    |
+| 0.0009        | 76.0  | 9500  | 2.1612          | 0.799    |
+| 0.0026        | 80.0  | 10000 | 2.1481          | 0.807    |
+| 0.0           | 84.0  | 10500 | 2.1813          | 0.807    |
+| 0.0           | 88.0  | 11000 | 2.2069          | 0.807    |
+| 0.0           | 92.0  | 11500 | 2.2285          | 0.807    |
+| 0.0           | 96.0  | 12000 | 2.2422          | 0.807    |
+| 0.0004        | 100.0 | 12500 | 2.2672          | 0.805    |
+
+
+### Framework versions
+
+- Transformers 4.40.1
+- Pytorch 2.3.0
+- Datasets 2.19.0
+- Tokenizers 0.19.1

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": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "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": "4.40.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

model.safetensors

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training_args.bin

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