Instructions to use Adorg/cm231104 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Adorg/cm231104 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Adorg/cm231104", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Adorg/cm231104", trust_remote_code=True) model = AutoModel.from_pretrained("Adorg/cm231104", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "additional_special_tokens": [ | |
| "[NF]", | |
| "[NI]", | |
| "[PMT]", | |
| "[PMT-SEP]", | |
| "[NPF]", | |
| "[NPI]" | |
| ], | |
| "bos_token": "[BOS]", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[BOS]", | |
| "eos_token": "[EOS]", | |
| "mask_token": "[MASK]", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "unk_token": "[UNK]" | |
| } | |