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 split_distribution.csv from beethogedeon/UltraFinBERT-150M: direct link, hf CLI and curl.
- Browser
- Download file 110 Bytes
-
https://huggingface.co/beethogedeon/UltraFinBERT-150M/resolve/main/split_distribution.csv
- Command line
-
hf download hf://beethogedeon/UltraFinBERT-150M/split_distribution.csv
-
curl -L -o split_distribution.csv https://huggingface.co/beethogedeon/UltraFinBERT-150M/resolve/main/split_distribution.csv
110 Bytes
| label_name,train,validation,test | |
| negative,212,45,46 | |
| neutral,970,208,208 | |
| positive,399,86,85 | |
| total,1581,339,339 | |