Feature Extraction
Transformers
Safetensors
English
roberta
recommendation
information retrieval
Amazon Reviews 2023
text-embeddings-inference
Instructions to use hyp1231/blair-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hyp1231/blair-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hyp1231/blair-roberta-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hyp1231/blair-roberta-base") model = AutoModel.from_pretrained("hyp1231/blair-roberta-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64ec7cb4bb1265a551218ad52ae2d006cb2e6f7e9a2991accc06df046194cfc5
- Size of remote file:
- 499 MB
- SHA256:
- dc8059cad4fd19d74ee9ad3733ea95e66fad9e7ee4645137d6019a75b6c040af
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