Instructions to use EMBEDDIA/sloberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBEDDIA/sloberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="EMBEDDIA/sloberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/sloberta") model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/sloberta", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from EMBEDDIA/sloberta: direct link, hf CLI and curl.
- Browser
- Download file 506 Bytes
-
https://huggingface.co/EMBEDDIA/sloberta/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://EMBEDDIA/sloberta/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/EMBEDDIA/sloberta/resolve/main/tokenizer_config.json
506 Bytes
| {"bos_token": "<s>", "eos_token": "</s>", "sep_token": "</s>", "cls_token": "<s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "additional_special_tokens": ["<s>NOTUSED", "</s>NOTUSED"], "special_tokens_map_file": null, "name_or_path": "EMBEDDIA/sloberta", "sp_model_kwargs": {}, "tokenizer_class": "CamembertTokenizer", "model_max_length": 512, "do_lower_case": false} | |