README.md

metadata

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 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
• 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)

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)

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
• 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 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
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):

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! 🚀🧠🔥