sandeshrajx/bert-fraud-classification-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: test_trainer_20240918_1411
+  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/ehdpl54g)
+[<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/ehdpl54g)
+# test_trainer_20240918_1411
+
+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: 0.6431
+- F1: 0.7435
+- Precision: 0.6605
+- Val Accuracy: {'accuracy': 0.706}
+
+## 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: 32
+- eval_batch_size: 16
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | F1     | Precision | Val Accuracy        |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:-------------------:|
+| 0.601         | 0.32  | 40   | 0.6152          | 0.7039 | 0.6597    | {'accuracy': 0.682} |
+| 0.6332        | 0.64  | 80   | 0.6143          | 0.7068 | 0.6515    | {'accuracy': 0.679} |
+| 0.4862        | 0.96  | 120  | 0.5791          | 0.7151 | 0.7137    | {'accuracy': 0.714} |
+| 0.6297        | 1.28  | 160  | 0.6323          | 0.7281 | 0.6495    | {'accuracy': 0.69}  |
+| 0.6164        | 1.6   | 200  | 0.5010          | 0.7522 | 0.8345    | {'accuracy': 0.774} |
+| 0.6333        | 1.92  | 240  | 0.5824          | 0.7310 | 0.6828    | {'accuracy': 0.71}  |
+| 0.4465        | 2.24  | 280  | 0.5335          | 0.7579 | 0.7695    | {'accuracy': 0.761} |
+| 0.5342        | 2.56  | 320  | 0.5065          | 0.7644 | 0.8040    | {'accuracy': 0.775} |
+| 0.5462        | 2.88  | 360  | 0.6431          | 0.7435 | 0.6605    | {'accuracy': 0.706} |
+
+
+### Framework versions
+
+- Transformers 4.45.0.dev0
+- Pytorch 2.4.0+cu121
+- Datasets 3.0.0
+- 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": "4.45.0.dev0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 105879
+}

model.safetensors

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+size 669455360

training_args.bin

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