Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,65 @@
+---
+language: en
+license: mit
+base_model: google-bert/bert-base-uncased
+tags:
+- token-classification
+- bert-base-uncased
+datasets:
+- disham993/ElectricalNER
+metrics:
+- epoch: 1.0
+- eval_precision: 0.8835414301929625
+- eval_recall: 0.9227851102505334
+- eval_f1: 0.9027369723210142
+- eval_accuracy: 0.956991714467814
+- eval_runtime: 2.6822
+- eval_samples_per_second: 562.603
+- eval_steps_per_second: 8.948
+---
+
+# disham993/electrical-ner-bert-base
+
+## Model description
+
+This model is fine-tuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) for token-classification tasks.
+
+## Training Data
+
+The model was trained on the disham993/ElectricalNER dataset. 
+
+## Model Details
+- **Base Model:** google-bert/bert-base-uncased
+- **Task:** token-classification
+- **Language:** en
+- **Dataset:** disham993/ElectricalNER
+
+## Training procedure
+
+### Training hyperparameters
+[Please add your training hyperparameters here]
+
+## Evaluation results
+
+### Metrics\n- epoch: 1.0\n- eval_precision: 0.8835414301929625\n- eval_recall: 0.9227851102505334\n- eval_f1: 0.9027369723210142\n- eval_accuracy: 0.956991714467814\n- eval_runtime: 2.6822\n- eval_samples_per_second: 562.603\n- eval_steps_per_second: 8.948
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("disham993/electrical-ner-bert-base")
+model = AutoModel.from_pretrained("disham993/electrical-ner-bert-base")
+```
+
+## Limitations and bias
+
+[Add any known limitations or biases of the model]
+
+## Training Infrastructure
+
+[Add details about training infrastructure used]
+
+## Last update
+
+2024-12-30