Instructions to use jparmet/topic-model-new-labels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jparmet/topic-model-new-labels with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import BertClassifier model = BertClassifier.from_pretrained("jparmet/topic-model-new-labels", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from jparmet/topic-model-new-labels: direct link, hf CLI and curl.
- Browser
- Download file 410 Bytes
-
https://huggingface.co/jparmet/topic-model-new-labels/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://jparmet/topic-model-new-labels/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/jparmet/topic-model-new-labels/resolve/main/tokenizer_config.json
410 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "max_length": 512, | |
| "model_max_length": 512, | |
| "pad_token": "[PAD]", | |
| "padding": "max_length", | |
| "return_tensors": "pt", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "truncation": true, | |
| "unk_token": "[UNK]" | |
| } | |