Text Classification
Transformers
PyTorch
TensorBoard
mpnet
Generated from Trainer
text-embeddings-inference
Instructions to use jayavibhav/mpnet-classification-10ksamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/mpnet-classification-10ksamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/mpnet-classification-10ksamples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/mpnet-classification-10ksamples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/mpnet-classification-10ksamples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jayavibhav/mpnet-classification-10ksamples: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/jayavibhav/mpnet-classification-10ksamples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jayavibhav/mpnet-classification-10ksamples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jayavibhav/mpnet-classification-10ksamples/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- e3acbb7e44d4e18a57d3f1890139cfe88e164e3e5fb27f7edbb80d206b8f2217
- Size of remote file:
- 438 MB
- SHA256:
- 4d62e979d4975a27893c9b0f4eafe8c08484e4a27bc09d58b81c831c0aceca98
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