Text Classification
Transformers
ONNX
Safetensors
multilingual
xlm-roberta
sentiment-analysis
social-media
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hailay/glot500-multilingual-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hailay/glot500-multilingual-sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hailay/glot500-multilingual-sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Hailay/glot500-multilingual-sentiment") model = AutoModelForSequenceClassification.from_pretrained("Hailay/glot500-multilingual-sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1479043e781f345c347ae6576464ff202b26721777d5e93e07814534cd303fd
- Size of remote file:
- 26.9 MB
- SHA256:
- 1628667eeef083e1ae8fc9db7ebd75542f51ce3524287da23931571285d935cb
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