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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from sagard21/python-code-explainer: direct link, hf CLI and curl.
- Browser
- Download file 2.95 GB
-
https://huggingface.co/sagard21/python-code-explainer/resolve/main/model.safetensors
- Command line
-
hf download hf://sagard21/python-code-explainer/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/sagard21/python-code-explainer/resolve/main/model.safetensors
2.95 GB
- Xet hash:
- ab27cd078d2af26757f748a5ea7e14beb3d5188fc0dcda0eaf5441e85972b13a
- Size of remote file:
- 2.95 GB
- SHA256:
- 8b8a32ab413be2b42ac6a21ac09453c1193a621a6b9a270d6be67f16e58ec00c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.