Instructions to use clem/autonlp-test3-2101787 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clem/autonlp-test3-2101787 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="clem/autonlp-test3-2101787")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("clem/autonlp-test3-2101787") model = AutoModelForSequenceClassification.from_pretrained("clem/autonlp-test3-2101787", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from clem/autonlp-test3-2101787: direct link, hf CLI and curl.
- Browser
- Download file 318 Bytes
-
https://huggingface.co/clem/autonlp-test3-2101787/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://clem/autonlp-test3-2101787/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/clem/autonlp-test3-2101787/resolve/main/tokenizer_config.json
318 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": "AutoNLP", "tokenizer_class": "DistilBertTokenizer"} |