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 /Nov25_11-24-44_tsf-508-wpa-1-223.epfl.ch /events.out.tfevents.1764066286.tsf-508-wpa-1-223.epfl.ch.6615.0
- Xet hash:
- 9558a1a05927e78a5360a2b0e29f0dbc07c90ee81bd269ae6f33a3c7ef95f338
- Size of remote file:
- 7.07 kB
- SHA256:
- bfd7d4a701d263c69d1bebe0f5446f4dfa5689fabf54f35a411bd95f30864b9a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.