Text Classification
Transformers
Safetensors
English
bert
deep-learning
huggingface
text-embeddings-inference
Instructions to use Driisa/finbert-finetuned-github with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Driisa/finbert-finetuned-github with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Driisa/finbert-finetuned-github")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Driisa/finbert-finetuned-github") model = AutoModelForSequenceClassification.from_pretrained("Driisa/finbert-finetuned-github") - Notebooks
- Google Colab
- Kaggle
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# **FinBERT Fine-Tuned on Financial Sentiment (Financial PhraseBank + GitHub Dataset)**
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model-name: "my_model"
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## **📌 Model Description**
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This model is a fine-tuned version of **FinBERT** (`ProsusAI/finbert`) trained for **financial sentiment classification**.
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It can classify financial text into **three categories**:
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# **FinBERT Fine-Tuned on Financial Sentiment (Financial PhraseBank + GitHub Dataset)**
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model-name: "my_model"
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## **📌 Model Description**
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This model is a fine-tuned version of **FinBERT** (`ProsusAI/finbert`) trained for **financial sentiment classification**.
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It can classify financial text into **three categories**:
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