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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57b53150ba3c0bbe487220d4659f730017fc5f43bacdbb5c5114c4ece8104c40
- Size of remote file:
- 1.7 GB
- SHA256:
- b8437f2c23a5fedd65cf4f6dea86f29041f560415e9d7c734305eccc85c787ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.