Text Classification
Transformers
Safetensors
English
deberta-v2
cross-encoder
reward-model
forecasting
text-embeddings-inference
Instructions to use HiddenSystemsAI/rem-matcher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiddenSystemsAI/rem-matcher with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HiddenSystemsAI/rem-matcher")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HiddenSystemsAI/rem-matcher") model = AutoModelForSequenceClassification.from_pretrained("HiddenSystemsAI/rem-matcher", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 572 Bytes
fe5f8ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"add_prefix_space": true,
"backend": "tokenizers",
"bos_token": "[CLS]",
"cls_token": "[CLS]",
"do_lower_case": false,
"eos_token": "[SEP]",
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"max_length": 512,
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"sp_model_kwargs": {},
"split_by_punct": false,
"stride": 0,
"tokenizer_class": "DebertaV2Tokenizer",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_id": 3,
"unk_token": "[UNK]",
"vocab_type": "spm"
}
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