Instructions to use anaghasavit/mysamplemodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anaghasavit/mysamplemodel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="anaghasavit/mysamplemodel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("anaghasavit/mysamplemodel") model = AutoModelForSequenceClassification.from_pretrained("anaghasavit/mysamplemodel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 538 Bytes
c75c043 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": true,
"mask_token": "[MASK]",
"model_max_length": 512,
"never_split": null,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"special_tokens_map_file": "/home/ukp-reimers/.cache/huggingface/transformers/448f85f42d7f87f0254da1997bc5cd60cb4607800084132993017232e82432a3.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
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