takala/financial_phrasebank
Updated • 6.64k • 269
How to use Farshid/bert-large-uncased-financial-phrasebank-allagree2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="Farshid/bert-large-uncased-financial-phrasebank-allagree2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Farshid/bert-large-uncased-financial-phrasebank-allagree2")
model = AutoModelForSequenceClassification.from_pretrained("Farshid/bert-large-uncased-financial-phrasebank-allagree2", device_map="auto")This model is a fine-tuned version of bert-large-uncased on the financial_phrasebank 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 | F1 |
|---|---|---|---|---|---|
| 0.3209 | 1.0 | 227 | 0.1929 | 0.9558 | 0.9551 |
| 0.0821 | 2.0 | 454 | 0.0994 | 0.9867 | 0.9867 |
| 0.04 | 3.0 | 681 | 0.0685 | 0.9867 | 0.9866 |
| 0.0098 | 4.0 | 908 | 0.0980 | 0.9867 | 0.9867 |
| 0.0003 | 5.0 | 1135 | 0.0734 | 0.9912 | 0.9911 |