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:
- 1b8af6dabeade77a8cfba7c256faa73fc1a8ed0ae3b72f589ec77cbfc31de49f
- Size of remote file:
- 4.41 kB
- SHA256:
- 43938e359195311e71d79513d481aa8256a8d41b767e728de8e24d73eafed513
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