Text Classification
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use RantiRepo/job_classifier_xlmroberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RantiRepo/job_classifier_xlmroberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RantiRepo/job_classifier_xlmroberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RantiRepo/job_classifier_xlmroberta") model = AutoModelForSequenceClassification.from_pretrained("RantiRepo/job_classifier_xlmroberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from RantiRepo/job_classifier_xlmroberta: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/RantiRepo/job_classifier_xlmroberta/resolve/main/tokenizer.json
- Command line
-
hf download hf://RantiRepo/job_classifier_xlmroberta/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/RantiRepo/job_classifier_xlmroberta/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- def26f1402a8b3768f136280933a5fb1ae919de7cff35c50196b7d49871a9830
- Size of remote file:
- 17.1 MB
- SHA256:
- 93189c5d9a15db043017cfd920e00cf72fe9a4220bd74b460b635f6aa85a61a2
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