{
    "model_id": "AhmedTaha012/finance-ner-v0.0.8-finetuned-ner",
    "downloads": 27954,
    "tags": [
        "transformers",
        "pytorch",
        "tensorboard",
        "bert",
        "token-classification",
        "generated_from_trainer",
        "license:mit",
        "autotrain_compatible",
        "endpoints_compatible",
        "region:us"
    ],
    "description": "--- license: mit tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finance-ner-v0.0.8-finetuned-ner 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. --> # finance-ner-v0.0.8-finetuned-ner This model is a fine-tuned version of dslim/bert-base-NER on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Precision: 0.9994 - Recall: 0.9997 - F1: 0.9995 - Accuracy: 1.0000 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0 | 1.0 | 4176 | 0.0001 | 0.9993 | 0.9998 | 0.9996 | 1.0000 | ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3",
    "model_explanation_gemini": "Detects financial entities in text by fine-tuning BERT-base-NER, achieving near-perfect precision, recall, and accuracy.  \n\n**Features:**  \n- Fine-tuned from `dslim/bert-base-NER`  \n- Metrics: 99.94% precision, 99.97% recall, 99.95% F1, 100% accuracy  \n- Training hyperparameters: 2e-5 learning rate, Adam optimizer, 1 epoch  \n\n**Comparison:**",
    "release_year": null,
    "parameter_count": null,
    "is_fine_tuned": true,
    "category": "Named Entity Recognition",
    "model_family": "BERT",
    "api_enhanced": true
}