Text Classification
Transformers
Safetensors
English
xlm-roberta
skill-detection
sentence-classification
ESCO
text-embeddings-inference
Instructions to use nurlanm/ESCOXLM-R_ENG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nurlanm/ESCOXLM-R_ENG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nurlanm/ESCOXLM-R_ENG")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nurlanm/ESCOXLM-R_ENG") model = AutoModelForSequenceClassification.from_pretrained("nurlanm/ESCOXLM-R_ENG") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c757e6c6eb0ee65e792afa32f79f464ce6eec90d05383784696e12e81fe7f2b7
- Size of remote file:
- 2.24 GB
- SHA256:
- 68c6e2f6df1d7bd8aacd39000f8e2badff34b5445f02bd552b601f3d5ba493cd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.