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
| {"do_lower_case": false, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "bos_token": "[CLS]", "eos_token": "[EOS]", "name_or_path": "/remote-home/yfshao/workdir/code-base/Megatron-LM/init_models_ckpt/cpt/large", "special_tokens_map_file": "vocab/cpt_v3_vocab/special_tokens_map.json", "tokenizer_file": null} |