Instructions to use wanggenfu/bert-textClassification-0.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wanggenfu/bert-textClassification-0.1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wanggenfu/bert-textClassification-0.1B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wanggenfu/bert-textClassification-0.1B") model = AutoModelForSequenceClassification.from_pretrained("wanggenfu/bert-textClassification-0.1B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from wanggenfu/bert-textClassification-0.1B: direct link, hf CLI and curl.
- Browser
- Download file 322 Bytes
-
https://huggingface.co/wanggenfu/bert-textClassification-0.1B/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://wanggenfu/bert-textClassification-0.1B/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/wanggenfu/bert-textClassification-0.1B/resolve/main/tokenizer_config.json
322 Bytes
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "do_lower_case": true, | |
| "is_local": false, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |