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 experiment.json from beethogedeon/UltraFinBERT-150M: direct link, hf CLI and curl.
- Browser
- Download file 2.04 kB
-
https://huggingface.co/beethogedeon/UltraFinBERT-150M/resolve/main/experiment.json
- Command line
-
hf download hf://beethogedeon/UltraFinBERT-150M/experiment.json
-
curl -L -o experiment.json https://huggingface.co/beethogedeon/UltraFinBERT-150M/resolve/main/experiment.json
2.04 kB
| { | |
| "base_model": "dmis-lab/UltraBERT-150M", | |
| "hub_repo_id": "beethogedeon/UltraFinBERT-150M", | |
| "model_revisions": { | |
| "dmis-lab/UltraBERT-150M": "4cc8ca415eb1958184c491297fe5aaa94c4a8ad2", | |
| "ProsusAI/finbert": "0bc2ac10dda6929bcb1f77ccc43c7ec5c424f031", | |
| "beethogedeon/Modern-FinBERT-large": "a5dd04dae984a46e729698ca39f398181c9aaeea" | |
| }, | |
| "seed": 42, | |
| "label2id": { | |
| "negative": 0, | |
| "neutral": 1, | |
| "positive": 2 | |
| }, | |
| "max_length": 256, | |
| "split_strategy": "Stratified 70/15/15 split generated from TXT with seed 42 (fallback mode)", | |
| "learning_rates": [ | |
| 8e-06 | |
| ], | |
| "max_epochs": 10, | |
| "early_stopping_patience": 3, | |
| "train_batch_size": 8, | |
| "gradient_accumulation_steps": 4, | |
| "effective_batch_size": 32, | |
| "optimizer": "AdamW", | |
| "betas": [ | |
| 0.9, | |
| 0.98 | |
| ], | |
| "epsilon": 1e-06, | |
| "weight_decay": 0.1, | |
| "scheduler": "linear", | |
| "warmup_fraction": 0.05, | |
| "training_compute_dtype": "bfloat16", | |
| "evaluation_compute_dtype": "torch.float16", | |
| "export_dtype": "float16", | |
| "attention": "sdpa", | |
| "data_audit": { | |
| "raw_rows": 2264, | |
| "encoding": "cp1252", | |
| "conflict_groups": 0, | |
| "conflict_rows_removed": 0, | |
| "duplicate_rows_removed": 5, | |
| "retained_rows": 2259, | |
| "raw_label_counts": { | |
| "neutral": 1391, | |
| "positive": 570, | |
| "negative": 303 | |
| }, | |
| "retained_label_counts": { | |
| "neutral": 1386, | |
| "positive": 570, | |
| "negative": 303 | |
| }, | |
| "source_sha256": "5fb370b3005b9a2759f708849a691ad2e4d2593d4c995c1afde229398a516594", | |
| "source": "provided TXT file", | |
| "split_strategy_note": "Stratified 70/15/15 split generated from TXT with seed 42 (fallback mode)", | |
| "split_sizes": { | |
| "train": 1581, | |
| "validation": 339, | |
| "test": 339 | |
| } | |
| }, | |
| "environment": { | |
| "torch": "2.11.0+cu128", | |
| "transformers": "5.16.1", | |
| "accelerate": "1.14.0", | |
| "datasets": "4.8.5", | |
| "huggingface_hub": "1.29.0", | |
| "safetensors": "0.8.0", | |
| "numpy": "2.1.3", | |
| "pandas": "2.3.3", | |
| "scikit-learn": "1.6.1" | |
| }, | |
| "device": "Tesla T4" | |
| } |