Text Classification
Transformers
Safetensors
English
bert
sentiment-analysis
finance
english
text-embeddings-inference
Instructions to use marcev/financebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use marcev/financebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="marcev/financebert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("marcev/financebert") model = AutoModelForSequenceClassification.from_pretrained("marcev/financebert") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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tokenizer = AutoTokenizer.from_pretrained('
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model = AutoModelForSequenceClassification.from_pretrained('marcev/financebert')
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def predict(text):
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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tokenizer = AutoTokenizer.from_pretrained('marcev/financebert')
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model = AutoModelForSequenceClassification.from_pretrained('marcev/financebert')
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def predict(text):
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