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:
- 1fb5b51f7d65d56096cdfbb5ce30ce28dc29a2695d4b01ef3b4f8972b2845fd6
- Size of remote file:
- 1.11 GB
- SHA256:
- 93316a86051c359748c5d5453e7660c69a21a57cfb477892f95f539e3e171196
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