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
File size: 237 Bytes
43e4ec6 | 1 2 3 4 5 6 7 8 9 | {
"epoch": 3.0,
"eval_accuracy": 0.664927536231884,
"eval_f1": 0.7987465181058496,
"eval_loss": 0.6411632299423218,
"eval_runtime": 8.9718,
"eval_samples_per_second": 192.269,
"eval_steps_per_second": 24.075
} |