Instructions to use furrutiav/neobert_mixtral_nllfg_rubric_qnli_sentence_embd_centroid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use furrutiav/neobert_mixtral_nllfg_rubric_qnli_sentence_embd_centroid with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="furrutiav/neobert_mixtral_nllfg_rubric_qnli_sentence_embd_centroid", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("furrutiav/neobert_mixtral_nllfg_rubric_qnli_sentence_embd_centroid", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86e45a57fd636a967c39eae852e5690a39a8e5ae62e415f67b45495797fddc12
- Size of remote file:
- 887 MB
- SHA256:
- 11a1c4ec2849bd2c40cd6d2ed9dd15978c99bf49fcf8b00f56a9561476b2b35f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.