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 selection.json from beethogedeon/UltraFinBERT-150M: direct link, hf CLI and curl.
- Browser
- Download file 363 Bytes
-
https://huggingface.co/beethogedeon/UltraFinBERT-150M/resolve/main/selection.json
- Command line
-
hf download hf://beethogedeon/UltraFinBERT-150M/selection.json
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curl -L -o selection.json https://huggingface.co/beethogedeon/UltraFinBERT-150M/resolve/main/selection.json
363 Bytes
| { | |
| "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 | |
| } |