Instructions to use hf-internal-testing/tiny-random-rembert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-rembert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-rembert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-rembert") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-rembert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": false, "remove_space": true, "keep_accents": true, "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"}, "model_max_length": 256, "special_tokens_map_file": "/home/lysandre/.cache/huggingface/transformers/d0d5cf448e7367ce69a8cbb48980c788a66b736ec136a0d3061fd26b5c1b25f0.f886166424e457f0fc75f92e81205faabe843b2dbbbef6b25f9d8ec69f64bc7d", "name_or_path": "google/rembert", "tokenizer_class": "RemBertTokenizer"} |