Instructions to use CeroShrijver/m3e-base-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CeroShrijver/m3e-base-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CeroShrijver/m3e-base-text-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CeroShrijver/m3e-base-text-classification") model = AutoModelForSequenceClassification.from_pretrained("CeroShrijver/m3e-base-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CeroShrijver/m3e-base-text-classification: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/CeroShrijver/m3e-base-text-classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://CeroShrijver/m3e-base-text-classification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CeroShrijver/m3e-base-text-classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
409 MB
- Xet hash:
- 035211455398e7a5ac44c79faae03586f34c0c796e3c74ec47b120d25168bffd
- Size of remote file:
- 409 MB
- SHA256:
- 21347efb15ca78d04c4f0d2cb6f2874b983b1273edb971536925369093dd3192
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