Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

9mark9
/
finbert-minilm-sentiment

Text Classification
Transformers
Safetensors
English
bert
financial-sentiment
finance
sentiment-analysis
minilm
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use 9mark9/finbert-minilm-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use 9mark9/finbert-minilm-sentiment with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="9mark9/finbert-minilm-sentiment")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("9mark9/finbert-minilm-sentiment")
    model = AutoModelForSequenceClassification.from_pretrained("9mark9/finbert-minilm-sentiment")
  • Notebooks
  • Google Colab
  • Kaggle
finbert-minilm-sentiment
134 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
9mark9's picture
9mark9
docs: rename to finbert-minilm-sentiment; link from-scratch nano-finbert project
fdbfec0 verified 14 days ago
  • .gitattributes
    1.52 kB
    initial commit 14 days ago
  • LICENSE
    1.07 kB
    Add trained NanoFinBERT checkpoint, tokenizer, source, config, model card 14 days ago
  • README.md
    6.07 kB
    docs: rename to finbert-minilm-sentiment; link from-scratch nano-finbert project 14 days ago
  • config.json
    847 Bytes
    feat: retrain on Financial PhraseBank β€” 95.29% test accuracy 14 days ago
  • model.safetensors
    133 MB
    xet
    feat: retrain on Financial PhraseBank β€” 95.29% test accuracy 14 days ago
  • tokenizer.json
    712 kB
    feat: retrain on Financial PhraseBank β€” 95.29% test accuracy 14 days ago
  • tokenizer_config.json
    379 Bytes
    feat: retrain on Financial PhraseBank β€” 95.29% test accuracy 14 days ago