Instructions to use witiko/mathberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use witiko/mathberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="witiko/mathberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("witiko/mathberta") model = AutoModelForMaskedLM.from_pretrained("witiko/mathberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from witiko/mathberta: direct link, hf CLI and curl.
- Browser
- Download file 348 Bytes
-
https://huggingface.co/witiko/mathberta/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://witiko/mathberta/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/witiko/mathberta/resolve/main/tokenizer_config.json
348 Bytes
| {"errors": "replace", "bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask>", "add_prefix_space": true, "trim_offsets": true, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "roberta-base", "tokenizer_class": "RobertaTokenizer"} |