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 fp16_validation.json from beethogedeon/UltraFinBERT-150M: direct link, hf CLI and curl.
- Browser
- Download file 385 Bytes
-
https://huggingface.co/beethogedeon/UltraFinBERT-150M/resolve/main/fp16_validation.json
- Command line
-
hf download hf://beethogedeon/UltraFinBERT-150M/fp16_validation.json
-
curl -L -o fp16_validation.json https://huggingface.co/beethogedeon/UltraFinBERT-150M/resolve/main/fp16_validation.json
385 Bytes
| { | |
| "accuracy": 0.9941002949852508, | |
| "balanced_accuracy": 0.9922480620155039, | |
| "macro_f1": 0.9888320545609548, | |
| "weighted_f1": 0.9941297180665263, | |
| "macro_precision": 0.9858156028368795, | |
| "macro_recall": 0.9922480620155039, | |
| "log_loss": 0.048102747473030165, | |
| "pre_export_validation_macro_f1": 0.9888320545609548, | |
| "macro_f1_delta_after_export": 0.0, | |
| "truncation_rate": 0.0 | |
| } |