Instructions to use hizella/aBERT_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hizella/aBERT_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hizella/aBERT_test")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hizella/aBERT_test") model = AutoModelForMaskedLM.from_pretrained("hizella/aBERT_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from hizella/aBERT_test: direct link, hf CLI and curl.
- Browser
- Download file 479 Bytes
-
https://huggingface.co/hizella/aBERT_test/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://hizella/aBERT_test/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/hizella/aBERT_test/resolve/main/tokenizer_config.json
479 Bytes
| {"do_lower_case": true, "remove_space": true, "keep_accents": false, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "<unk>", "sep_token": "[SEP]", "pad_token": "<pad>", "cls_token": "[CLS]", "mask_token": {"content": "[MASK]", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "sp_model_kwargs": {}, "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "aBERT", "tokenizer_class": "AlbertTokenizer"} |