clinc/clinc_oos
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How to use patnelt60/distilbert-base-uncased-distilled-squad-finetuned-clinc with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="patnelt60/distilbert-base-uncased-distilled-squad-finetuned-clinc") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("patnelt60/distilbert-base-uncased-distilled-squad-finetuned-clinc")
model = AutoModelForSequenceClassification.from_pretrained("patnelt60/distilbert-base-uncased-distilled-squad-finetuned-clinc", device_map="auto")This model is a fine-tuned version of distilbert-base-uncased-distilled-squad 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 | 40 | 3.7816 | 0.2016 |
| No log | 2.0 | 80 | 3.3589 | 0.5374 |
| No log | 3.0 | 120 | 2.9695 | 0.6955 |
| No log | 4.0 | 160 | 2.6408 | 0.7726 |
| No log | 5.0 | 200 | 2.3697 | 0.8145 |
| No log | 6.0 | 240 | 2.1547 | 0.8426 |
| No log | 7.0 | 280 | 1.9912 | 0.8529 |
| 2.8639 | 8.0 | 320 | 1.8802 | 0.8645 |
| 2.8639 | 9.0 | 360 | 1.8138 | 0.8706 |
| 2.8639 | 10.0 | 400 | 1.7920 | 0.8723 |