clinc/clinc_oos
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How to use 54data/distilbert-base-uncased-finetuned-clinc with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="54data/distilbert-base-uncased-finetuned-clinc") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("54data/distilbert-base-uncased-finetuned-clinc")
model = AutoModelForSequenceClassification.from_pretrained("54data/distilbert-base-uncased-finetuned-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:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 4.2924 | 1.0 | 318 | 3.2763 | 0.7284 |
| 2.6141 | 2.0 | 636 | 1.8625 | 0.8365 |
| 1.5389 | 3.0 | 954 | 1.1513 | 0.8984 |
| 1.0087 | 4.0 | 1272 | 0.8540 | 0.9135 |
| 0.793 | 5.0 | 1590 | 0.7725 | 0.9165 |
Base model
distilbert/distilbert-base-uncased