Instructions to use Taykhoom/structRFM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Taykhoom/structRFM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Taykhoom/structRFM", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("Taykhoom/structRFM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Taykhoom/structRFM: direct link, hf CLI and curl.
- Browser
- Download file 363 Bytes
-
https://huggingface.co/Taykhoom/structRFM/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Taykhoom/structRFM/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Taykhoom/structRFM/resolve/main/tokenizer_config.json
363 Bytes
| { | |
| "auto_map": { | |
| "AutoTokenizer": [ | |
| "tokenization_structrfm.StructRFMTokenizer", | |
| null | |
| ] | |
| }, | |
| "tokenizer_class": "StructRFMTokenizer", | |
| "model_max_length": 514, | |
| "padding_side": "right", | |
| "truncation_side": "right", | |
| "cls_token": "[CLS]", | |
| "sep_token": "[SEP]", | |
| "pad_token": "[PAD]", | |
| "mask_token": "[MASK]", | |
| "unk_token": "[UNK]" | |
| } |