Update README.md

README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- bert
+- text-classification
+- autotrain
+- runashllm
+- custom-model
+datasets:
+- your_dataset_name_here
+metrics:
+- accuracy
+- f1
+widget:
+- text: I love this model!
+- text: This is terrible.
+model-index:
+- name: RunAshLLM
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: YourDataset
+      type: your_dataset_name_here
+    metrics:
+    - type: accuracy
+      value: 0.92
+    - type: f1
+      value: 0.91
+title: 'RunAshLLM '
+colorFrom: yellow
+pinned: true
+short_description: 'Custom BERT Model Fine-Tuned '
+---
+
+
+# 🚀 RunAshLLM — Custom BERT Model Fine-Tuned with AutoTrain
+
+**RunAshLLM** is a fine-tuned [BERT-base-uncased](https://huggingface.co/bert-base-uncased) model, optimized for text classification tasks using **Hugging Face AutoTrain**. Designed for speed, accuracy, and adaptability — whether you're classifying sentiment, intent, or custom categories.
+
+---
+
+## 🧪 Model Details
+
+- **Base Model**: `bert-base-uncased`
+- **Fine-tuning Tool**: [AutoTrain Advanced](https://huggingface.co/autotrain)
+- **Task**: Text Classification (adjustable)
+- **Language**: English
+- **Architecture**: `BertForSequenceClassification`
+- **Parameters**: ~110M
+
+---
+
+## 💡 Intended Uses
+
+RunAshLLM is ideal for:
+
+- Sentiment analysis (positive/negative/neutral)
+- Customer feedback categorization
+- Custom domain classification (e.g., medical, legal, finance)
+- Educational or research prototyping
+
+> ⚠️ Not intended for production without further validation and testing.
+
+---
+
+## 🛠️ How to Use
+
+### With `pipeline` (Simplest)
+
+```python
+from transformers import pipeline
+
+classifier = pipeline("text-classification", model="your-hf-username/RunAshLLM")
+
+result = classifier("I love using AutoTrain to fine-tune models!")
+print(result)
+# Output: [{'label': 'POSITIVE', 'score': 0.987}]
+
+### With Automodel (Advance )
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("your-hf-username/RunAshLLM")
+model = AutoModelForSequenceClassification.from_pretrained("your-hf-username/RunAshLLM")
+
+inputs = tokenizer("This model is awesome!", return_tensors="pt")
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+predicted_class_id = logits.argmax().item()
+label = model.config.id2label[predicted_class_id]
+print(label)  # e.g., "POSITIVE"
+
+
+Absolutely! Below is a complete, ready-to-use **Hugging Face BERT model configuration** and **customized model card** for a model named **`RunAshLLM`**, intended to be fine-tuned using **AutoTrain**.
+
+This includes:
+
+1. ✅ `config.json` — BERT configuration (you can adjust architecture)
+2. ✅ `README.md` — Custom Model Card for Hugging Face Hub
+3. ✅ Instructions for AutoTrain fine-tuning
+
 ---
-title: Bert Finetuning Project
-emoji: 🚀
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
-short_description: runash-custom-llm-space
-hf_oauth: true
-hf_oauth_expiration_minutes: 36000
-hf_oauth_scopes:
-- read-repos
-- write-repos
-- manage-repos
-- inference-api
-- read-billing
-tags:
-- autotrain
-license: apache-2.0
----
-
-# Docs
-
-https://huggingface.co/docs/autotrain
-
-# Citation
-
-@misc{thakur2024autotrainnocodetrainingstateoftheart,
-      title={AutoTrain: No-code training for state-of-the-art models}, 
-      author={Abhishek Thakur},
-      year={2024},
-      eprint={2410.15735},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI},
-      url={https://arxiv.org/abs/2410.15735}, 
+
+## 🧠 1. `config.json` — BERT Base Configuration (Customizable)
+
+Save this as `config.json` in your model repo or AutoTrain project folder.
+
+```json
+{
+  "architectures": ["BertForSequenceClassification"],
+  "model_type": "bert",
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 512,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "type_vocab_size": 2,
+  "vocab_size": 30522,
+  "classifier_dropout": 0.1,
+  "num_labels": 2,
+  "id2label": {
+    "0": "NEGATIVE",
+    "1": "POSITIVE"
+  },
+  "label2id": {
+    "NEGATIVE": 0,
+    "POSITIVE": 1
+  }
 }
+```
+
+> 🔧 *Customize `num_labels`, `id2label`, `label2id` based on your task (e.g., multiclass, NER, QA).*
+
+---
+
+### With `AutoModel` (Advanced)
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("your-hf-username/RunAshLLM")
+model = AutoModelForSequenceClassification.from_pretrained("your-hf-username/RunAshLLM")
+
+inputs = tokenizer("This model is awesome!", return_tensors="pt")
+with torch.no_grad():
+    logits = model(**inputs).logits
+
+predicted_class_id = logits.argmax().item()
+label = model.config.id2label[predicted_class_id]
+print(label)  # e.g., "POSITIVE"
+```
+
+---
+
+## 📊 Evaluation Results
+
+| Metric  | Score |
+|---------|-------|
+| Accuracy | 92%   |
+| F1-Score | 91%   |
+
+> *Results based on held-out test set from `YourDataset`. Your mileage may vary.*
+
+---
+
+## 🎯 Training Details
+
+- **Training Framework**: AutoTrain Advanced
+- **Dataset**: [YourDataset](https://huggingface.co/datasets/your_dataset_name_here)
+- **Epochs**: 3
+- **Batch Size**: 16
+- **Learning Rate**: 2e-5
+- **Optimizer**: AdamW
+- **Hardware**: 1x NVIDIA T4 (via AutoTrain)
+
+---
+
+## 📜 License
+
+Apache 2.0 — Feel free to use, modify, and distribute. See [LICENSE](LICENSE) for details.
+
+---
+
+## 🙌 Acknowledgements
+
+- Hugging Face 🤗 for AutoTrain and Transformers
+- Original BERT authors and maintainers
+- You — for pushing the boundaries of what fine-tuned models can do!
+
+---
+
+> **Model Name Inspired By**: “Run Ash, Run!” — A playful nod to resilience, speed, and the spirit of experimentation.
+
+---
+
+## ❓ Questions?
+
+Open an Issue on the model repository or reach out on Hugging Face forums.
+
+---
+
+✨ **Made with AutoTrain. Deployed with confidence.**
+```
+
+> ✏️ **Remember to replace**:
+> - `your-hf-rammurmu/RunAshLLM` → your actual Hugging Face model repo path
+> - `your_dataset_name_here` → your dataset name
+> - Evaluation scores → your actual metrics
+> - License → if you choose a different one
+
+---
+
+## ⚙️ 3. AutoTrain Setup Instructions
+
+### Step 1: Prepare Dataset
+- Format: CSV or Hugging Face Dataset
+- Required columns: `text`, `label` (for classification)
+
+Example `train.csv`:
+```csv
+text,label
+"I love this!",1
+"This is awful.",0
+```
+
+### Step 2: Use AutoTrain CLI or Web UI
+
+#### Web UI (Easiest):
+1. Go to [https://huggingface.co/autotrain](https://huggingface.co/autotrain)
+2. Click “Create Project”
+3. Upload dataset
+4. Choose “Text Classification”
+5. Select `bert-base-uncased` as base model
+6. Set project name: `RunAshLLM`
+7. Start training!
+
+#### CLI (Advanced):
+```bash
+pip install autotrain-advanced
+
+autotrain llm --help  # for LLMs, but for BERT classification:
+
+autotrain text-classification \
+  --model bert-base-uncased \
+  --data_path ./data \
+  --project_name RunAshLLM \
+  --token YOUR_HF_TOKEN \
+  --push_to_hub
+```
+
+---
+
+## 📁 Final Folder Structure (for manual upload)
+
+```
+RunAshLLM/
+├── config.json
+├── README.md
+├── LICENSE (optional)
+└── (AutoTrain will generate model weights after training)
+```
+
+---
+
+## ✅ After Training
+
+AutoTrain will automatically:
+
+- Upload model weights (`pytorch_model.bin`, `tf_model.h5`, etc.)
+- Push tokenizer files
+- Update model card if configured
+
+You just need to ensure your `README.md` and `config.json` are in the repo root.
+
+---
+
+## 🎉 Happy fine-tuning! 🚀🧠🔥