Instructions to use denritchie/tBERT-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use denritchie/tBERT-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="denritchie/tBERT-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("denritchie/tBERT-v1") model = AutoModelForMaskedLM.from_pretrained("denritchie/tBERT-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from denritchie/tBERT-v1: direct link, hf CLI and curl.
- Browser
- Download file 166 Bytes
-
https://huggingface.co/denritchie/tBERT-v1/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://denritchie/tBERT-v1/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/denritchie/tBERT-v1/resolve/main/tokenizer_config.json
166 Bytes
| {"max_len": 512, "bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": "<mask>"} |