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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```py
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from transformers import AutoTokenizer, T5ForConditionalGeneration, SummarizationPipeline
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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pipeline = SummarizationPipeline(
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model=T5ForConditionalGeneration.from_pretrained("sagard21/python-code-explainer"),
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tokenizer=AutoTokenizer.from_pretrained("sagard21/python-code-explainer", skip_special_tokens=True),
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```py
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from transformers import AutoTokenizer, T5ForConditionalGeneration, SummarizationPipeline
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pipeline = SummarizationPipeline(
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model=T5ForConditionalGeneration.from_pretrained("sagard21/python-code-explainer"),
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tokenizer=AutoTokenizer.from_pretrained("sagard21/python-code-explainer", skip_special_tokens=True),
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