Text Classification
Transformers
PyTorch
Safetensors
Chinese
bart
text2text-generation
fill-mask
Summarization
Chinese
CPT
BART
BERT
seq2seq
Instructions to use OpenMOSS-Team/cpt-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/cpt-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OpenMOSS-Team/cpt-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("OpenMOSS-Team/cpt-large") model = AutoModelForSeq2SeqLM.from_pretrained("OpenMOSS-Team/cpt-large") - Notebooks
- Google Colab
- Kaggle
Yunfan Shao commited on
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# Chinese
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## Model description
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This is an implementation of CPT-Large. To use CPT, please import the file `modeling_cpt.py` (**Download** [Here](https://github.com/fastnlp/CPT/blob/master/finetune/modeling_cpt.py)) that define the architecture of CPT into your project.
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# Chinese CPT-Large
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## Model description
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This is an implementation of CPT-Large. To use CPT, please import the file `modeling_cpt.py` (**Download** [Here](https://github.com/fastnlp/CPT/blob/master/finetune/modeling_cpt.py)) that define the architecture of CPT into your project.
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