Text Classification
Transformers
Joblib
Safetensors
bert
sentiment-analysis
finance
macroeconomics
climate
esg
policy
ensemble
dictionary
finbert
Eval Results (legacy)
text-embeddings-inference
Instructions to use peyterho/macro-sentiment-finbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peyterho/macro-sentiment-finbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="peyterho/macro-sentiment-finbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("peyterho/macro-sentiment-finbert") model = AutoModelForSequenceClassification.from_pretrained("peyterho/macro-sentiment-finbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,258 Bytes
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"v0.1.0_baselines": {
"dict_only": {
"accuracy": 0.5684,
"f1_macro": 0.5277,
"f1_weighted": 0.5784,
"n_samples": 4333,
"method": "LM + Henry polarity composite, threshold ±0.05"
},
"meta_classifier_dict": {
"accuracy": 0.6693,
"f1_macro": 0.5781,
"f1_weighted": 0.6500,
"method": "GradientBoosting on 24 dictionary features"
}
},
"v0.2.0_finetuned": {
"finbert": {
"accuracy": 0.8973,
"f1_macro": 0.8813,
"f1_weighted": 0.8984,
"n_samples": 4333,
"model": "peyterho/finbert-macro-sentiment",
"base_model": "ProsusAI/finbert",
"params": "110M"
},
"roberta_large": {
"accuracy": 0.9130,
"f1_macro": 0.9023,
"f1_weighted": 0.9137,
"n_samples": 4333,
"model": "peyterho/financial-roberta-large-macro-sentiment",
"base_model": "soleimanian/financial-roberta-large-sentiment",
"params": "355M"
},
"climatebert": {
"accuracy": 0.8885,
"f1_macro": 0.8716,
"f1_weighted": 0.8898,
"n_samples": 4333,
"model": "peyterho/climatebert-macro-sentiment",
"base_model": "climatebert/distilroberta-base-climate-sentiment",
"params": "82M"
}
}
}
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