clinc/clinc_oos
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How to use optimum/roberta-large-finetuned-clinc with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="optimum/roberta-large-finetuned-clinc") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("optimum/roberta-large-finetuned-clinc")
model = AutoModelForSequenceClassification.from_pretrained("optimum/roberta-large-finetuned-clinc")This model is a fine-tuned version of roberta-large on the clinc_oos dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 239 | 0.8113 | 0.9035 |
| No log | 2.0 | 478 | 0.2364 | 0.9548 |
| 1.7328 | 3.0 | 717 | 0.1760 | 0.9684 |
| 1.7328 | 4.0 | 956 | 0.1565 | 0.9723 |
| 0.0976 | 5.0 | 1195 | 0.1574 | 0.9729 |