Harshraj8721
/

agri_finetuned_model

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-## Uses
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ---
 library_name: transformers
+license: mit
+datasets:
+- worldbank-datause/PRWP
+base_model:
+- openai-community/gpt2
+pipeline_tag: text-generation
 ---
+Model Card for Your Model
+Model Details
+Model Description
+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.
+Developed by: [Harshraj Bhoite]
+Funded by: Self-funded
+Shared by: [Harshraj]
+Model type: Transformer-based ( GPT-2)
+Language(s) (NLP): English
+License: Apache 2.0 / MIT / Custom
+Finetuned from model: [GPT-2] (e.g., GPT-2, BERT, T5)
+Model Sources
+Repository: [https://huggingface.co/Harshraj8721/agri_finetuned_model]
+Uses
+Direct Use
+AI-assisted data engineering documentation generation
+Business intelligence reports and data insights automation
+Technical content creation for AI and analytics
+Downstream Use
+Fine-tuning for Agriculture-specific AI
+Conversational AI in data analytics applications
+AI-driven customer support for analytics tools
+Out-of-Scope Use
+Not intended for real-time conversational AI without further optimization
+May not perform well in non-English languages
+Bias, Risks, and Limitations
+Bias: Model performance may be influenced by the dataset used.
+Limitations: It may generate inaccurate or misleading responses in highly technical scenarios.
+Mitigation: Users should validate outputs for critical decision-making.
+How to Get Started with the Model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "Harshraj8721/agri_finetuned_model"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+input_text = "Explain Delta Lake architecture"
+inputs = tokenizer(input_text, return_tensors="pt")
+output = model.generate(**inputs)
+print(tokenizer.decode(output[0]))
+Training Details
+Training Data
+Dataset: Proprietary dataset of technical blogs, data engineering articles, and structured datasets.
+Preprocessing: Tokenization with Byte Pair Encoding (BPE) or WordPiece.
+Training Procedure
+Hyperparameters
+Batch size: 16
+Learning rate: 3e-5
+Precision: fp16 mixed precision
+Optimizer: AdamW
+Compute Infrastructure
+Hardware: NVIDIA A100 GPUs (x4)
+Cloud Provider: AWS / Azure / GCP
+Training Duration: ~36 hours
+Evaluation
+Testing Data, Factors & Metrics
+Testing Data
+Synthetic datasets from AI-powered analytics use cases
+Real-world structured datasets from data engineering pipelines
+Metrics
+Perplexity (PPL): Measures how well the model predicts text
+BLEU Score: Evaluates generated text quality
+F1 Score: Measures precision and recall
+Results
+Perplexity: 9.7 (lower is better)
+BLEU Score: 34.2 (higher is better)
+F1 Score: 85.5%
+Environmental Impact
+Hardware Type: NVIDIA A100 GPUs
+Hours used: 36 hours
+Carbon Emitted: ~50 kg CO2eq (estimated using ML CO2 Impact Calculator)
+Citation
+If you use this model, please cite it as follows:
+@misc{Harshraj8721/agri_finetuned_model/2025,
+  title={agri_finetuned_model},
+  author={Harshraj Bhoite},
+  year={2025},
+  url={https://huggingface.co/Harshraj8721/agri_finetuned_model}
+}
+Contact
+For queries, reach out to:
+Email: harshraj8721@gmail.com
+LinkedIn: Linkedin/in/harshrajb/