Text Classification
Transformers
Safetensors
English
modernbert
dissatisfaction
user-feedback
conversational-ai
semantic-routing
sentiment
text-embeddings-inference
Instructions to use vllm-sr/feedback-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/feedback-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vllm-sr/feedback-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vllm-sr/feedback-detector") model = AutoModelForSequenceClassification.from_pretrained("vllm-sr/feedback-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from vllm-sr/feedback-detector: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/vllm-sr/feedback-detector/resolve/main/training_args.bin
- Command line
-
hf download hf://vllm-sr/feedback-detector/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vllm-sr/feedback-detector/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- c3b36a9bad4f06ab7ca7161d1f5b296323c4e83eb216531c9d370c61ba143201
- Size of remote file:
- 5.84 kB
- SHA256:
- 9eadab910a9232cd9cb5c6ac868fbf4c4f3cb1fa1bd21483ffbd1f14d021e977
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