Instructions to use foundkim/topic_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use foundkim/topic_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="foundkim/topic_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("foundkim/topic_classifier") model = AutoModelForSequenceClassification.from_pretrained("foundkim/topic_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from foundkim/topic_classifier: direct link, hf CLI and curl.
- Browser
- Download file 320 Bytes
-
https://huggingface.co/foundkim/topic_classifier/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://foundkim/topic_classifier/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/foundkim/topic_classifier/resolve/main/tokenizer_config.json
320 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-cased", "tokenizer_class": "BertTokenizer"} |