Text Classification
Transformers
Safetensors
Vietnamese
xlm-roberta
vietnamese
uit-vsmec
transfer
eacl-2027
emotion-recognition
social-media
Instructions to use BaoNhan/cafebert-UITVSMEC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BaoNhan/cafebert-UITVSMEC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BaoNhan/cafebert-UITVSMEC")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("BaoNhan/cafebert-UITVSMEC") model = AutoModelForSequenceClassification.from_pretrained("BaoNhan/cafebert-UITVSMEC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1703a589a4d9967a9ca93361026dd588d253b1e09fd6a7896d0f91853754451b
- Size of remote file:
- 5.5 kB
- SHA256:
- ad1dd69a8c696bc12c64ea70517309c2be840670fe60ccda108cc5a2633445ac
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