clinc/clinc_oos
Viewer • Updated • 59.3k • 8.43k • 20
How to use davidaponte/kd-distilBERT-clinc with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="davidaponte/kd-distilBERT-clinc") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("davidaponte/kd-distilBERT-clinc")
model = AutoModelForSequenceClassification.from_pretrained("davidaponte/kd-distilBERT-clinc")This model is a fine-tuned version of distilbert-base-uncased on the clinc_oos dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.3211 | 1.0 | 318 | 3.3313 | 0.7235 |
| 2.6568 | 2.0 | 636 | 1.9016 | 0.8452 |
| 1.5575 | 3.0 | 954 | 1.1668 | 0.8955 |
| 1.0094 | 4.0 | 1272 | 0.8619 | 0.9087 |
| 0.7914 | 5.0 | 1590 | 0.7752 | 0.9129 |