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README.md
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@@ -51,10 +51,27 @@ outputs = model.generate(
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for i in range(4):
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print(tokenizer.decode(outputs[i], skip_special_tokens=True, clean_up_tokenization_spaces=True))
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# The mechanical engineering field requires an understanding of core areas including mechanics, dynamics, thermodynamics, materials science, structural analysis, and electricity.
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# ... Developing the knowledge base for these core areas will enable engineers to build their capabilities rapidly and efficiently. ...
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# ... The field of mechanics offers a variety and broad range for applications throughout the engineering/technological fields. ...
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# ... Mechanics generally is not understood by students. While they can be employed in the field, mechanical engineering ...
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# ... Introduction to mechanical engineering and core fields including chemical products, materials science, structural analysis, and geomatics ...
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for i in range(4):
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print(tokenizer.decode(outputs[i], skip_special_tokens=True, clean_up_tokenization_spaces=True))
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# INPUT: The mechanical engineering field requires an understanding of core areas including mechanics, dynamics, thermodynamics, materials science, structural analysis, and electricity.
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# > Cheapity3 continues with beam search:
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# ... The field of mechanical engineering is a broad field that includes many core areas of engineering.
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# > Cheapity3 continues with sampling and top_k=50:
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# ... Developing the knowledge base for these core areas will enable engineers to build their capabilities rapidly and efficiently. ...
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# ... The field of mechanics offers a variety and broad range for applications throughout the engineering/technological fields. ...
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# ... Mechanics generally is not understood by students. While they can be employed in the field, mechanical engineering ...
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# ... Introduction to mechanical engineering and core fields including chemical products, materials science, structural analysis, and geomatics ...
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```
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## Pretty decent right?
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Hence, whenever you feel like GPT3 is too expensive, Cheapity3 comes to the rescue 🤗.
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## Model Training FYI
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- T5-base model
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- Trained on only 1M sentences from English, French and German text
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- Mostly text from wikipedia, arxiv and QA datasets
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- Learning rate: 0.00003
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- 2 epochs
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- Max input: 512 tokens
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- Max output: 128 tokens
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