Instructions to use Shruthikaa/FNet_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shruthikaa/FNet_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shruthikaa/FNet_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shruthikaa/FNet_Classification") model = AutoModelForSequenceClassification.from_pretrained("Shruthikaa/FNet_Classification") - Notebooks
- Google Colab
- Kaggle
File size: 491 Bytes
a9baed6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"clean_up_tokenization_spaces": true,
"cls_token": "[CLS]",
"do_lower_case": false,
"keep_accents": true,
"mask_token": {
"__type": "AddedToken",
"content": "[MASK]",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"model_max_length": 512,
"pad_token": "<pad>",
"remove_space": true,
"sep_token": "[SEP]",
"sp_model_kwargs": {},
"tokenizer_class": "FNetTokenizer",
"unk_token": "<unk>"
}
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