Instructions to use PoetschLab/GROVER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PoetschLab/GROVER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="PoetschLab/GROVER")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("PoetschLab/GROVER") model = AutoModelForMaskedLM.from_pretrained("PoetschLab/GROVER", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from PoetschLab/GROVER: direct link, hf CLI and curl.
- Browser
- Download file 314 Bytes
-
https://huggingface.co/PoetschLab/GROVER/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://PoetschLab/GROVER/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/PoetschLab/GROVER/resolve/main/tokenizer_config.json
314 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "do_lower_case": false, | |
| "model_max_length": 512, | |
| "special_tokens": { | |
| "mask_token": "[MASK]", | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]" | |
| }, | |
| "tokenize_chinese_chars": false, | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "trust_remote_code": true | |
| } | |