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: 287 Bytes
fe5f8ee | 1 2 3 4 5 6 7 8 9 10 | {
"temperature": 1.7073526474706913,
"bias": -0.4470148298452159,
"decision_threshold": 0.5,
"confidence_threshold": 0.8,
"method": "platt_on_logit_margin",
"positive_label": "yes",
"probability_scope": "L2 matching on the held-out pair distribution",
"max_length": 512
} |