Translation
Transformers
PyTorch
JAX
Rust
t5
text2text-generation
summarization
text-generation-inference
Instructions to use qiaoyi/Comment_Summarization4DesignTutor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qiaoyi/Comment_Summarization4DesignTutor with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="qiaoyi/Comment_Summarization4DesignTutor")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("qiaoyi/Comment_Summarization4DesignTutor") model = AutoModelForSeq2SeqLM.from_pretrained("qiaoyi/Comment_Summarization4DesignTutor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4975d7920ad9103fbf4b72ee2638a1a433fcb5da9cb10fd4351b0adb35a848dc
- Size of remote file:
- 242 MB
- SHA256:
- 636b17628823af7a43631a408e0253a5fe9ae39ea02a87ec8d3371ea613e5b16
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.