Text Classification
Transformers
TensorFlow
xlm-roberta
generated_from_keras_callback
text-embeddings-inference
Instructions to use cruiser/roberta_tensorflow_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cruiser/roberta_tensorflow_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cruiser/roberta_tensorflow_test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cruiser/roberta_tensorflow_test") model = AutoModelForSequenceClassification.from_pretrained("cruiser/roberta_tensorflow_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbbe037f0cbf40b2bcc3ed65c03752770eef40c7500df6dced623f6a6d64bf89
- Size of remote file:
- 1.11 GB
- SHA256:
- b17b655befabf83eba344e17e3653dc44dc687b377f14f2edf92afc986c8d220
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