Summarization
Transformers
PyTorch
Safetensors
English
t5
text2text-generation
Trained with AutoTrain
text-generation-inference
Instructions to use sagard21/python-code-explainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagard21/python-code-explainer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="sagard21/python-code-explainer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sagard21/python-code-explainer") model = AutoModelForSeq2SeqLM.from_pretrained("sagard21/python-code-explainer") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- sagard21/autotrain-data-code-explainer
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def preprocess(text: str) -> str:
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text = str(text)
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text = text.replace('\\n', ' ')
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tokenized_text = text.split(' ')
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preprocessed_text = " ".join([token for token in tokenized_text if token])
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return preprocessed_text
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datasets:
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- sagard21/autotrain-data-code-explainer
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co2_eq_emissions:
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