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tsessk
/
content

Text Classification
Transformers
Safetensors
llama
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use tsessk/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use tsessk/content with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="tsessk/content")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("tsessk/content")
    model = AutoModelForSequenceClassification.from_pretrained("tsessk/content")
  • Notebooks
  • Google Colab
  • Kaggle
content / sample_data
56.8 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
tsessk's picture
tsessk
tsessk/llm-course-hw2-reward-model
dfcf4f1 verified about 1 year ago
  • README.md
    962 Bytes
    tsessk/llm-course-hw2-reward-model about 1 year ago
  • anscombe.json
    1.7 kB
    tsessk/llm-course-hw2-reward-model about 1 year ago
  • california_housing_test.csv
    301 kB
    tsessk/llm-course-hw2-reward-model about 1 year ago
  • california_housing_train.csv
    1.71 MB
    tsessk/llm-course-hw2-reward-model about 1 year ago
  • mnist_test.csv
    18.3 MB
    xet
    tsessk/llm-course-hw2-reward-model about 1 year ago
  • mnist_train_small.csv
    36.5 MB
    xet
    tsessk/llm-course-hw2-reward-model about 1 year ago