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
Add evaluation results
Browse files- eval_results.json +22 -0
eval_results.json
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{
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"dict_only": {
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"accuracy": 0.5684,
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"f1_macro": 0.5277,
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"f1_weighted": 0.5784,
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"n_samples": 4333,
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"method": "LM + Henry polarity composite, threshold ±0.05"
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},
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"meta_classifier_dict": {
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"accuracy": 0.6693,
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"f1_macro": 0.5781,
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"f1_weighted": 0.6500,
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"method": "GradientBoosting on 24 dictionary features"
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},
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"full_pipeline": {
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"accuracy": 0.6200,
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"f1_macro": 0.6012,
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"f1_weighted": 0.6347,
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"n_samples": 100,
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"method": "Transformer ensemble (FinBERT/RoBERTa/ClimateBERT) + dictionary signals, routed"
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}
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}
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