README.md

---

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

---

## 🧠 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! 🚀🧠🔥