Text Classification
Transformers
Safetensors
Chinese
English
bert
sales
intent-classification
dialogue
evaluation
Instructions to use MultiSense/SaleIntent_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MultiSense/SaleIntent_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MultiSense/SaleIntent_bert")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MultiSense/SaleIntent_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d96cb8d0f8bd1a8e0771ceb903c65ef9dc504c485960b5202f5d3a280bf73ff5
- Size of remote file:
- 5.24 kB
- SHA256:
- f84e3f88b99ca02168e94f3afcb84a32763725debad38771758947c2743ab233
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.