Text Classification
Transformers
Safetensors
multilingual
xlm-roberta
cross-encoder
reranker
feed-ranking
enterprise-feed
learning-to-rank
text-embeddings-inference
Instructions to use FDS-Iterations/third-pass-feed-ranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FDS-Iterations/third-pass-feed-ranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FDS-Iterations/third-pass-feed-ranker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FDS-Iterations/third-pass-feed-ranker") model = AutoModelForSequenceClassification.from_pretrained("FDS-Iterations/third-pass-feed-ranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98b9cbe6b688900f4e50fa5bbf2b3090003a6a93dd21cac3d383c4cca48b10c0
- Size of remote file:
- 17.1 MB
- SHA256:
- 7e46326c545ff6da42f5474c4845979f724e537dcc2ad6c20bc061b73b5f9472
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