Text Classification
Transformers
Safetensors
English
ultrabert
finance
sentiment-analysis
financial-sentiment-analysis
custom_code
fp16
Eval Results (legacy)
Instructions to use beethogedeon/UltraFinBERT-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beethogedeon/UltraFinBERT-150M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="beethogedeon/UltraFinBERT-150M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("beethogedeon/UltraFinBERT-150M", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("beethogedeon/UltraFinBERT-150M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 363 Bytes
42dd9d2 | 1 2 3 4 5 6 7 8 9 10 11 | {
"learning_rate": 8e-06,
"validation_macro_f1": 0.9888320545609548,
"validation_accuracy": 0.9941002949852508,
"validation_loss": 0.04801607131958008,
"trained_epochs": 8.0,
"best_checkpoint": "ultrafinbert_runs/20261016T154623284896Z/trials/lr_8e-06/checkpoint-250",
"global_steps": 400,
"warmup_steps": 25,
"training_seconds": 450.235867831
} |