Instructions to use VARabi/Sentence-ALDi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VARabi/Sentence-ALDi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VARabi/Sentence-ALDi")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VARabi/Sentence-ALDi") model = AutoModelForSequenceClassification.from_pretrained("VARabi/Sentence-ALDi", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from VARabi/Sentence-ALDi: direct link, hf CLI and curl.
- Browser
- Download file 376 Bytes
-
https://huggingface.co/VARabi/Sentence-ALDi/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://VARabi/Sentence-ALDi/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/VARabi/Sentence-ALDi/resolve/main/tokenizer_config.json
376 Bytes
| {"do_lower_case": true, "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, "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "/project/6007993/elmadany/Models/MARBERT_17M/pytorch_verison/"} |