Text Classification
Transformers
TensorBoard
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use NurErtug/crowd_sourced_web_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NurErtug/crowd_sourced_web_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NurErtug/crowd_sourced_web_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NurErtug/crowd_sourced_web_classifier") model = AutoModelForSequenceClassification.from_pretrained("NurErtug/crowd_sourced_web_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
crowd_sourced_web_classifier / runs /Dec01_09-49-22_tsf-508-wpa-6-167.epfl.ch /events.out.tfevents.1764579048.tsf-508-wpa-6-167.epfl.ch.2424.2
- Xet hash:
- c1b242650411c08953207bac55b512e1c32686605eceec2e73d75f2ad93d9e1c
- Size of remote file:
- 411 Bytes
- SHA256:
- 269e117f39b8bb6ca1e87a9b1869870083386391157848239bdf33cf7fd71112
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