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--- |
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library_name: transformers |
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license: mit |
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datasets: |
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- worldbank-datause/PRWP |
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base_model: |
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- openai-community/gpt2 |
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pipeline_tag: text-generation |
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--- |
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Model Card for Your Model |
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Model Details |
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Model Description |
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This is a transformers-based model fine-tuned for generative AI tasks, particularly in data engineering and AI service applications. It has been optimized for structured text generation, analytics, and AI-assisted workflows. The model supports multi-turn interactions and is designed for business intelligence, data insights, and technical documentation generation. |
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Developed by: [Harshraj Bhoite] |
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Funded by: Self-funded |
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Shared by: [Harshraj] |
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Model type: Transformer-based ( GPT-2) |
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Language(s) (NLP): English |
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License: Apache 2.0 / MIT / Custom |
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Finetuned from model: [GPT-2] (e.g., GPT-2, BERT, T5) |
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Model Sources |
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Repository: [https://huggingface.co/Harshraj8721/agri_finetuned_model] |
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Uses |
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Direct Use |
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AI-assisted data engineering documentation generation |
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Business intelligence reports and data insights automation |
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Technical content creation for AI and analytics |
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Downstream Use |
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Fine-tuning for Agriculture-specific AI |
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Conversational AI in data analytics applications |
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AI-driven customer support for analytics tools |
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Out-of-Scope Use |
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Not intended for real-time conversational AI without further optimization |
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May not perform well in non-English languages |
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Bias, Risks, and Limitations |
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Bias: Model performance may be influenced by the dataset used. |
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Limitations: It may generate inaccurate or misleading responses in highly technical scenarios. |
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Mitigation: Users should validate outputs for critical decision-making. |
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How to Get Started with the Model |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "Harshraj8721/agri_finetuned_model" |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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input_text = "Explain Delta Lake architecture" |
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inputs = tokenizer(input_text, return_tensors="pt") |
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output = model.generate(**inputs) |
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print(tokenizer.decode(output[0])) |
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Training Details |
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Training Data |
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Dataset: Proprietary dataset of technical blogs, data engineering articles, and structured datasets. |
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Preprocessing: Tokenization with Byte Pair Encoding (BPE) or WordPiece. |
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Training Procedure |
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Hyperparameters |
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Batch size: 16 |
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Learning rate: 3e-5 |
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Precision: fp16 mixed precision |
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Optimizer: AdamW |
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Compute Infrastructure |
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Hardware: NVIDIA A100 GPUs (x4) |
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Cloud Provider: AWS / Azure / GCP |
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Training Duration: ~36 hours |
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Evaluation |
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Testing Data, Factors & Metrics |
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Testing Data |
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Synthetic datasets from AI-powered analytics use cases |
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Real-world structured datasets from data engineering pipelines |
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Metrics |
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Perplexity (PPL): Measures how well the model predicts text |
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BLEU Score: Evaluates generated text quality |
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F1 Score: Measures precision and recall |
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Results |
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Perplexity: 9.7 (lower is better) |
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BLEU Score: 34.2 (higher is better) |
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F1 Score: 85.5% |
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Environmental Impact |
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Hardware Type: NVIDIA A100 GPUs |
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Hours used: 36 hours |
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Carbon Emitted: ~50 kg CO2eq (estimated using ML CO2 Impact Calculator) |
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Citation |
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If you use this model, please cite it as follows: |
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@misc{Harshraj8721/agri_finetuned_model/2025, |
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title={agri_finetuned_model}, |
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author={Harshraj Bhoite}, |
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year={2025}, |
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url={https://huggingface.co/Harshraj8721/agri_finetuned_model} |
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} |
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Contact |
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For queries, reach out to: |
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Email: harshraj8721@gmail.com |
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LinkedIn: Linkedin/in/harshrajb/ |
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