Feature Extraction
Transformers
PyTorch
Vietnamese
xlm-roberta
vietnamese
contrastive-learning
sentence-embedding
natural-language-inference
low-resource
nlu
Instructions to use huynhtin/ViCLSR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huynhtin/ViCLSR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="huynhtin/ViCLSR")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, XLMRobertaForCL tokenizer = AutoTokenizer.from_pretrained("huynhtin/ViCLSR") model = XLMRobertaForCL.from_pretrained("huynhtin/ViCLSR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from huynhtin/ViCLSR: direct link, hf CLI and curl.
- Browser
- Download file 4.48 GB
-
https://huggingface.co/huynhtin/ViCLSR/resolve/refs%2Fpr%2F2/optimizer.pt
- Command line
-
hf download hf://huynhtin/ViCLSR@refs/pr/2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/huynhtin/ViCLSR/resolve/refs%2Fpr%2F2/optimizer.pt
4.48 GB
- Xet hash:
- 3d76eb6a978895bb33a9cca64e48b6ddb0eb3ea53b2e3e735b8d65017743233a
- Size of remote file:
- 4.48 GB
- SHA256:
- 6920d4d0ebc22eb189756113b3164469a5f0b722c7d63055353f20fe9d700951
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