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
Download test_comparison.csv from beethogedeon/UltraFinBERT-150M: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
-
https://huggingface.co/beethogedeon/UltraFinBERT-150M/resolve/main/test_comparison.csv
- Command line
-
hf download hf://beethogedeon/UltraFinBERT-150M/test_comparison.csv
-
curl -L -o test_comparison.csv https://huggingface.co/beethogedeon/UltraFinBERT-150M/resolve/main/test_comparison.csv
1.28 kB
| model,accuracy,balanced_accuracy,macro_f1,weighted_f1,macro_precision,macro_recall,log_loss,macro_f1_ci95_low,macro_f1_ci95_high,test_examples,truncation_rate,parameters,evaluation_dtype,revision,training_overlap_note | |
| beethogedeon/UltraFinBERT-150M,0.9646017699115044,0.9542543773362188,0.9615904929520475,0.9641735110013125,0.9702531262693043,0.9542543773362188,0.24856460459181326,0.9373423664558937,0.9814966497430953,339,0.0,151034883,torch.float16,local FP16 export,Held out from this fine-tuning run; upstream pretraining exposure not audited. | |
| ProsusAI/finbert,0.9823008849557522,0.9847408026755854,0.9775293265383634,0.9823943729157764,0.9708343294286398,0.9847408026755854,0.11372488239044834,0.9590552079097949,0.9945137250496199,339,0.0,109484547,torch.float16,0bc2ac10dda6929bcb1f77ccc43c7ec5c424f031,Previously fine-tuned on FinancialPhraseBank; overlap with this test is unknown and possible. | |
| beethogedeon/Modern-FinBERT-large,0.9823008849557522,0.9764705882352942,0.9748919274144381,0.9821485315052908,0.9748525002418029,0.9764705882352942,0.09822984893543157,0.9525722119021349,0.9916481561294743,339,0.0,395834371,torch.float16,a5dd04dae984a46e729698ca39f398181c9aaeea,Previously fine-tuned on FinancialPhraseBank; overlap with this test is unknown and possible. | |