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README.md

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+---
+language: en
+license: mit
+base_model: google-bert/bert-large-uncased
+tags:
+- text-classification
+- bert-large-uncased
+datasets:
+- disham993/ElectricalDeviceFeedbackBalanced
+metrics:
+- epoch: 1.0
+- eval_f1: 0.8665314714124963
+- eval_accuracy: 0.8683431952662722
+- eval_runtime: 2.6138
+- eval_samples_per_second: 517.252
+- eval_steps_per_second: 16.451
+---
+
+# disham993/electrical-classification-bert-large-uncased
+
+## Model description
+
+This model is fine-tuned from [google-bert/bert-large-uncased](https://huggingface.co/google-bert/bert-large-uncased) for text-classification tasks.
+
+## Training Data
+
+The model was trained on the disham993/ElectricalDeviceFeedbackBalanced dataset. 
+
+## Model Details
+- **Base Model:** google-bert/bert-large-uncased
+- **Task:** text-classification
+- **Language:** en
+- **Dataset:** disham993/ElectricalDeviceFeedbackBalanced
+
+## Training procedure
+
+### Training hyperparameters
+[Please add your training hyperparameters here]
+
+## Evaluation results
+
+### Metrics\n- epoch: 1.0\n- eval_f1: 0.8665314714124963\n- eval_accuracy: 0.8683431952662722\n- eval_runtime: 2.6138\n- eval_samples_per_second: 517.252\n- eval_steps_per_second: 16.451
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("disham993/electrical-classification-bert-large-uncased")
+model = AutoModel.from_pretrained("disham993/electrical-classification-bert-large-uncased")
+```
+
+## Limitations and bias
+
+[Add any known limitations or biases of the model]
+
+## Training Infrastructure
+
+[Add details about training infrastructure used]
+
+## Last update
+
+2025-01-05