Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use OliverHeine/microsoft_deberta-v3-base_train_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OliverHeine/microsoft_deberta-v3-base_train_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="OliverHeine/microsoft_deberta-v3-base_train_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("OliverHeine/microsoft_deberta-v3-base_train_v1") model = AutoModelForSequenceClassification.from_pretrained("OliverHeine/microsoft_deberta-v3-base_train_v1") - Notebooks
- Google Colab
- Kaggle
File size: 532 Bytes
f849dd0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"add_prefix_space": true,
"backend": "tokenizers",
"bos_token": "[CLS]",
"cls_token": "[CLS]",
"do_lower_case": false,
"eos_token": "[SEP]",
"extra_special_tokens": [
"[PAD]",
"[CLS]",
"[SEP]"
],
"is_local": false,
"mask_token": "[MASK]",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"split_by_punct": false,
"tokenizer_class": "DebertaV2Tokenizer",
"unk_id": 3,
"unk_token": "[UNK]",
"vocab_type": "spm"
}
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