test

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google-bert/bert-base-multilingual-uncased
+tags:
+- generated_from_trainer
+metrics:
+- f1
+- precision
+model-index:
+- name: bert-fraud-classification-test-mass
+  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. -->
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sandeshrajx/ultron-nlp/runs/bim0galx)
+# bert-fraud-classification-test-mass
+
+This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3963
+- F1: 0.8194
+- Precision: 0.8445
+- Val Accuracy: 0.8375
+
+## 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: 44
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 88
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 50
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | F1     | Precision | Val Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:------:|:---------:|:------------:|
+| 0.5197        | 0.1743 | 40   | 0.5468          | 0.7488 | 0.6907    | 0.7459       |
+| 0.5208        | 0.3486 | 80   | 0.4667          | 0.7687 | 0.7890    | 0.7911       |
+| 0.4235        | 0.5229 | 120  | 0.4351          | 0.7986 | 0.7898    | 0.8113       |
+| 0.404         | 0.6972 | 160  | 0.4577          | 0.7972 | 0.7751    | 0.8066       |
+| 0.3736        | 0.8715 | 200  | 0.4274          | 0.7914 | 0.8775    | 0.8240       |
+| 0.419         | 1.0458 | 240  | 0.4058          | 0.7912 | 0.8737    | 0.8232       |
+| 0.2701        | 1.2200 | 280  | 0.4075          | 0.8124 | 0.8393    | 0.8316       |
+| 0.4345        | 1.3943 | 320  | 0.4246          | 0.8110 | 0.8088    | 0.8244       |
+| 0.3258        | 1.5686 | 360  | 0.4023          | 0.7992 | 0.8788    | 0.8294       |
+| 0.3938        | 1.7429 | 400  | 0.3945          | 0.8174 | 0.8447    | 0.8361       |
+| 0.2529        | 1.9172 | 440  | 0.3963          | 0.8194 | 0.8445    | 0.8375       |
+
+
+### Framework versions
+
+- Transformers 4.46.0.dev0
+- Pytorch 2.4.1+cu121
+- Datasets 3.0.1
+- Tokenizers 0.20.0

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": "4.46.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

model.safetensors

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training_args.bin

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