Instructions to use hf-tiny-model-private/tiny-random-AlbertForSequenceClassification 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-AlbertForSequenceClassification 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-AlbertForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-AlbertForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-AlbertForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 286 Bytes
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"bos_token": "[CLS]",
"cls_token": "[CLS]",
"eos_token": "[SEP]",
"mask_token": {
"content": "[MASK]",
"lstrip": true,
"normalized": false,
"rstrip": false,
"single_word": false
},
"pad_token": "<pad>",
"sep_token": "[SEP]",
"unk_token": "<unk>"
}
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