Text Classification
Transformers
Safetensors
English
bert
Generated from Trainer
finance
text-embeddings-inference
Instructions to use samchain/EconoSentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use samchain/EconoSentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="samchain/EconoSentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("samchain/EconoSentiment") model = AutoModelForSequenceClassification.from_pretrained("samchain/EconoSentiment") - Notebooks
- Google Colab
- Kaggle
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README.md
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# EconoSentiment
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This model is a fine-tuned version of [samchain/econo-sentence-v2](https://huggingface.co/samchain/econo-sentence-v2) on the Financial Phrase Bank dataset from FinanceMTEB.
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It achieves the following results on the evaluation set:
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- Loss: 0.1293
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- Accuracy: 0.962
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# EconoSentiment
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This model is a fine-tuned version of [samchain/econo-sentence-v2](https://huggingface.co/samchain/econo-sentence-v2) on the Financial Phrase Bank dataset from FinanceMTEB.
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The full model is trained using a small learning rate isntead of freezing the encoder. Hence, you should not use the encoder of this model for a task other than sentiment analysis.
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It achieves the following results on the evaluation set:
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- Loss: 0.1293
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- Accuracy: 0.962
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