End of training

README.md

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+---
+license: apache-2.0
+base_model: google-bert/bert-base-multilingual-uncased
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: tes1-UASNLP2
+  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. -->
+
+# tes1-UASNLP2
+
+This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3497
+- Accuracy: 0.8732
+- Precision: 0.8735
+- Recall: 0.9022
+- F1: 0.8876
+
+## 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: 64
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 0.4694        | 1.2121 | 100  | 0.3865          | 0.8459   | 0.8288    | 0.9104 | 0.8677 |
+| 0.2551        | 2.4242 | 200  | 0.3497          | 0.8732   | 0.8735    | 0.9022 | 0.8876 |
+| 0.1637        | 3.6364 | 300  | 0.4273          | 0.8648   | 0.8426    | 0.9302 | 0.8843 |
+| 0.1013        | 4.8485 | 400  | 0.4728          | 0.8759   | 0.8863    | 0.8906 | 0.8884 |
+| 0.0656        | 6.0606 | 500  | 0.5119          | 0.8740   | 0.9053    | 0.8632 | 0.8838 |
+| 0.0445        | 7.2727 | 600  | 0.5829          | 0.8732   | 0.8852    | 0.8865 | 0.8859 |
+| 0.029         | 8.4848 | 700  | 0.6224          | 0.8747   | 0.8809    | 0.8953 | 0.8881 |
+| 0.0188        | 9.6970 | 800  | 0.6621          | 0.8751   | 0.8744    | 0.9049 | 0.8894 |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Tokenizers 0.19.1

config.json

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+{
+  "_name_or_path": "google-bert/bert-base-multilingual-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "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,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

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training_args.bin

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