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_10-44-03_tsf-508-wpa-6-167.epfl.ch /events.out.tfevents.1764582243.tsf-508-wpa-6-167.epfl.ch.3860.1
- Xet hash:
- 509a7cef340d47a49d40b7adc625dd8d44b5454ac8f4fe0eb440da2fa06d81e3
- Size of remote file:
- 17.4 kB
- SHA256:
- edcf46d5af545af312e4351d39609e8f2774e0b51d7e2edb56f99eba5391d617
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.