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
| { | |
| "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 | |
| } |