Instructions to use dfafdsaf/deberta_sentiment_5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dfafdsaf/deberta_sentiment_5000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dfafdsaf/deberta_sentiment_5000")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dfafdsaf/deberta_sentiment_5000") model = AutoModelForSequenceClassification.from_pretrained("dfafdsaf/deberta_sentiment_5000") - Notebooks
- Google Colab
- Kaggle
Upload tokenizer
Browse files- tokenizer.json +0 -0
- tokenizer_config.json +7 -0
tokenizer.json
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tokenizer_config.json
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@@ -48,12 +48,19 @@
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"eos_token": "[SEP]",
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"sp_model_kwargs": {},
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"split_by_punct": false,
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"tokenizer_class": "DebertaV2Tokenizer",
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"unk_token": "[UNK]",
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"vocab_type": "spm"
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}
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| 48 |
"eos_token": "[SEP]",
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"extra_special_tokens": {},
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"mask_token": "[MASK]",
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"max_length": 512,
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"model_max_length": 1000000000000000019884624838656,
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"pad_to_multiple_of": null,
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"pad_token": "[PAD]",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"sep_token": "[SEP]",
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"sp_model_kwargs": {},
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"split_by_punct": false,
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"stride": 0,
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"tokenizer_class": "DebertaV2Tokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "[UNK]",
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"vocab_type": "spm"
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}
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