Text Classification
Transformers
PyTorch
TensorFlow
Safetensors
multilingual
xlm-roberta
sentiment-analysis
twitter
text-embeddings-inference
Instructions to use NetworkIsLife/twitter-xlm-roberta-base-sentiment-fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NetworkIsLife/twitter-xlm-roberta-base-sentiment-fast with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="NetworkIsLife/twitter-xlm-roberta-base-sentiment-fast")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NetworkIsLife/twitter-xlm-roberta-base-sentiment-fast") model = AutoModelForSequenceClassification.from_pretrained("NetworkIsLife/twitter-xlm-roberta-base-sentiment-fast", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5ef79d1141e9c1e4bcf2f96818e56af2cb15464f0c8cb0a9a2f7f4160e6dc65
- Size of remote file:
- 17.1 MB
- SHA256:
- b74659c780d49afad7a7b9799868f75cbd3014fb6c34956e85a793028d38094a
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