Instructions to use melll-uff/itd_longformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use melll-uff/itd_longformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="melll-uff/itd_longformer")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("melll-uff/itd_longformer") model = AutoModelForMaskedLM.from_pretrained("melll-uff/itd_longformer", device_map="auto") - 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, "model_max_length": 4096, "special_tokens_map_file": "/root/.cache/torch/transformers/aa29d58ec2f0278e3ff71ce26a902511a549e75a7596ae790a751253f2950b3d.275045728fbf41c11d3dae08b8742c054377e18d92cc7b72b6351152a99b64e4", "full_tokenizer_file": null, "tokenizer_file": null, "name_or_path": "bert-base-4096"} |