clinc/clinc_oos
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How to use yuashi/voiceassistant-intent-distilbert-clinc150 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="yuashi/voiceassistant-intent-distilbert-clinc150") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("yuashi/voiceassistant-intent-distilbert-clinc150")
model = AutoModelForSequenceClassification.from_pretrained("yuashi/voiceassistant-intent-distilbert-clinc150", device_map="auto")This model is a fine-tuned version of distilbert-base-uncased on 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 | F1 Macro |
|---|---|---|---|---|---|
| No log | 1.0 | 477 | 1.8080 | 0.8313 | 0.8251 |
| 3.4255 | 2.0 | 954 | 0.5902 | 0.9229 | 0.9266 |
| 0.9815 | 3.0 | 1431 | 0.3297 | 0.9381 | 0.9423 |
| 0.3066 | 4.0 | 1908 | 0.2674 | 0.9442 | 0.9479 |
| 0.1389 | 5.0 | 2385 | 0.2574 | 0.9445 | 0.9482 |
Base model
distilbert/distilbert-base-uncased