Text Classification
Transformers
Safetensors
distilbert
sifter
redrob
reranker
reward-model
recruitment
explainable-ai
human-feedback
Eval Results (legacy)
text-embeddings-inference
Instructions to use shikharshahi/sifter-redrob-reranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shikharshahi/sifter-redrob-reranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shikharshahi/sifter-redrob-reranker")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shikharshahi/sifter-redrob-reranker") model = AutoModelForSequenceClassification.from_pretrained("shikharshahi/sifter-redrob-reranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shikharshahi/sifter-redrob-reranker: direct link, hf CLI and curl.
- Browser
- Download file 5.33 kB
-
https://huggingface.co/shikharshahi/sifter-redrob-reranker/resolve/main/training_args.bin
- Command line
-
hf download hf://shikharshahi/sifter-redrob-reranker/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shikharshahi/sifter-redrob-reranker/resolve/main/training_args.bin
5.33 kB
- Xet hash:
- 53c4821b598d3096b79de56b4ca05895cbcfe52d4983d36331122320d115444a
- Size of remote file:
- 5.33 kB
- SHA256:
- 7e642ca0cf853da39f16469c1788d22e2796e13c8b19c9afd05067cb3d832c9e
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