Text Classification
Transformers
Safetensors
English
bert
mbert
intent-classification
nlp
text-embeddings-inference
Instructions to use tyaisndu/mbert_intent_model_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tyaisndu/mbert_intent_model_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tyaisndu/mbert_intent_model_v3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tyaisndu/mbert_intent_model_v3") model = AutoModelForSequenceClassification.from_pretrained("tyaisndu/mbert_intent_model_v3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- aa2937254703c03fff1b7e58e388cb02ad8acce9fe8d23921b13502ca6cf2e7e
- Size of remote file:
- 5.14 kB
- SHA256:
- 6e70e166ccd4f9de2210ddca366a1c06fe318019084b080c33504d979eff5da4
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