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")# 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 tokenizer.json from vllm-sr/feedback-detector: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/vllm-sr/feedback-detector/resolve/main/tokenizer.json
- Command line
-
hf download hf://vllm-sr/feedback-detector/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/vllm-sr/feedback-detector/resolve/main/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.