Instructions to use sgugger/tiny-distilbert-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgugger/tiny-distilbert-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sgugger/tiny-distilbert-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sgugger/tiny-distilbert-classification") model = AutoModelForSequenceClassification.from_pretrained("sgugger/tiny-distilbert-classification") - Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": "tiny-distilbert-base-uncased-finetuned-sst-2-english/special_tokens_map.json", "name_or_path": "tiny-distilbert-base-uncased-finetuned-sst-2-english", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "DistilBertTokenizer"} |