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--- |
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license: mit |
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language: |
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- en |
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- multilingual |
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widget: |
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- text: 'Q: How can I increase the yield of my potato crop?' |
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example_title: example 1 |
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- text: 'Q: how do i check for corn maturity?' |
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example_title: example 2 |
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tags: |
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- agriculture |
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- agriculture llm |
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- agriculture qa |
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datasets: |
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- KisanVaani/agriculture-qa-english-only |
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--- |
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## Note |
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Introducing AgriQBot 🌾🤖: Embarking on the journey to cultivate knowledge in agriculture! 🚜🌱 Currently in its early testing phase, AgriQBot is a multilingual small language model dedicated to agriculture. 🌍🌾 As we harvest insights, the data generation phase is underway, and continuous improvement is the key. 🔄💡 The vision? Crafting a compact yet powerful model fueled by a high-quality dataset, with plans to fine-tune it for direct tasks in the future. |
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### Usage |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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pipe = pipeline("text2text-generation", model="mrSoul7766/AgriQBot") |
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# Example user query |
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user_query = "How can I increase the yield of my potato crop?" |
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# Generate response |
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answer = pipe(f"Q: {user_query}", max_length=512) |
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# Print the generated answer |
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print(answer[0]['generated_text']) |
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``` |
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### or |
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```python |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("mrSoul7766/AgriQBot") |
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model = AutoModelForSeq2SeqLM.from_pretrained("mrSoul7766/AgriQBot") |
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# Set maximum generation length |
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max_length = 512 |
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# Generate response with question as input |
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input_ids = tokenizer.encode("Q: How can I increase the yield of my potato crop?", return_tensors="pt") |
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output_ids = model.generate(input_ids, max_length=max_length) |
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# Decode response |
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response = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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print(response) |
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``` |
