Instructions to use hf-tiny-model-private/tiny-random-XLNetForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-XLNetForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-XLNetForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLNetForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-XLNetForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
File size: 345 Bytes
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"additional_special_tokens": [
"<eop>",
"<eod>"
],
"bos_token": "<s>",
"cls_token": "<cls>",
"eos_token": "</s>",
"mask_token": {
"content": "<mask>",
"lstrip": true,
"normalized": true,
"rstrip": false,
"single_word": false
},
"pad_token": "<pad>",
"sep_token": "<sep>",
"unk_token": "<unk>"
}
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