Instructions to use TJMUCH/transcriptome-iseeek with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TJMUCH/transcriptome-iseeek with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="TJMUCH/transcriptome-iseeek")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("TJMUCH/transcriptome-iseeek") model = AutoModelForMaskedLM.from_pretrained("TJMUCH/transcriptome-iseeek", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from TJMUCH/transcriptome-iseeek: direct link, hf CLI and curl.
- Browser
- Download file 158 Bytes
-
https://huggingface.co/TJMUCH/transcriptome-iseeek/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://TJMUCH/transcriptome-iseeek/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/TJMUCH/transcriptome-iseeek/resolve/main/tokenizer_config.json
158 Bytes
| {"mask_token": "[MASK]", "cls_token": "[CLS]", "sep_token": "[SEP]", "pad_token": "[PAD]", "unk_token": "[UNK]", "tokenizer_class": "PreTrainedTokenizerFast"} |