Instructions to use Intel/dynamic_tinybert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/dynamic_tinybert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Intel/dynamic_tinybert")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Intel/dynamic_tinybert") model = AutoModelForQuestionAnswering.from_pretrained("Intel/dynamic_tinybert", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Intel/dynamic_tinybert: direct link, hf CLI and curl.
- Browser
- Download file 351 Bytes
-
https://huggingface.co/Intel/dynamic_tinybert/resolve/refs%2Fpr%2F1/tokenizer_config.json
- Command line
-
hf download hf://Intel/dynamic_tinybert@refs/pr/1/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Intel/dynamic_tinybert/resolve/refs%2Fpr%2F1/tokenizer_config.json
351 Bytes
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "/store/nosnap/results/inter6_bert_24.8.13.50/checkpoint-last", "do_basic_tokenize": true, "never_split": null} |