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 /Dec02_16-18-56_tsf-508-wpa-2-088.epfl.ch /events.out.tfevents.1764688737.tsf-508-wpa-2-088.epfl.ch.7997.1
- Xet hash:
- 6899069c8112785e6c48e66456f97b0d82de1e3fc9afb0c71dd1302b3e68f065
- Size of remote file:
- 20.3 kB
- SHA256:
- 2a0fb3255a17806a2e14d76625d3b48098d0ae71ac089a616bce4a2f7d25624c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.