Text Classification
Transformers
PyTorch
TensorBoard
bert
Generated from Trainer
text-embeddings-inference
Instructions to use ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000") model = AutoModelForSequenceClassification.from_pretrained("ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000: direct link, hf CLI and curl.
- Browser
- Download file 322 Bytes
-
https://huggingface.co/ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/ASCCCCCCCC/distilbert-base-chinese-amazon_zh_20000/resolve/main/tokenizer_config.json
322 Bytes
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-chinese", "tokenizer_class": "BertTokenizer"} |