Text Classification
Transformers
PyTorch
TensorFlow
English
bert
financial-sentiment-analysis
sentiment-analysis
Instructions to use ldeb/solved-finbert-tone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ldeb/solved-finbert-tone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ldeb/solved-finbert-tone")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ldeb/solved-finbert-tone") model = AutoModelForSequenceClassification.from_pretrained("ldeb/solved-finbert-tone") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -0
config.json
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},
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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},
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"model_type": "bert",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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