takala/financial_phrasebank
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How to use Farshid/distilbert-base-uncased_allagree3 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Farshid/distilbert-base-uncased_allagree3") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Farshid/distilbert-base-uncased_allagree3")
model = AutoModelForSequenceClassification.from_pretrained("Farshid/distilbert-base-uncased_allagree3", device_map="auto")This model is a fine-tuned version of distilbert-base-uncased on the financial_phrasebank 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 |
|---|---|---|---|---|---|
| 0.6418 | 1.0 | 57 | 0.3340 | 0.8805 | 0.8768 |
| 0.1821 | 2.0 | 114 | 0.1088 | 0.9690 | 0.9691 |
| 0.0795 | 3.0 | 171 | 0.0822 | 0.9823 | 0.9823 |
| 0.0385 | 4.0 | 228 | 0.0939 | 0.9646 | 0.9646 |
| 0.0218 | 5.0 | 285 | 0.1151 | 0.9735 | 0.9737 |
| 0.0149 | 6.0 | 342 | 0.1126 | 0.9690 | 0.9694 |
| 0.006 | 7.0 | 399 | 0.0989 | 0.9779 | 0.9780 |
| 0.0093 | 8.0 | 456 | 0.1009 | 0.9779 | 0.9780 |
| 0.0063 | 9.0 | 513 | 0.0899 | 0.9779 | 0.9780 |
| 0.0039 | 10.0 | 570 | 0.0937 | 0.9779 | 0.9780 |