Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use EdwarV/NLP_sequences_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdwarV/NLP_sequences_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="EdwarV/NLP_sequences_example")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("EdwarV/NLP_sequences_example") model = AutoModelForSequenceClassification.from_pretrained("EdwarV/NLP_sequences_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7a5e484bde6c98748299718b27bf2b90bb0151c27a35d4d41fd92262de5f6571
- Size of remote file:
- 329 MB
- SHA256:
- 66ddb99142c32bd11470a9b46553511e6e81e817d06fbca4b7bedac96ae8b4af
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