Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use zkava01/toneconomyroberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zkava01/toneconomyroberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zkava01/toneconomyroberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zkava01/toneconomyroberta") model = AutoModelForSequenceClassification.from_pretrained("zkava01/toneconomyroberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.7331367135047913
f1_macro: 0.7363235984916315
f1_micro: 0.76
f1_weighted: 0.7496564127718217
precision_macro: 0.7844142785319256
precision_micro: 0.76
precision_weighted: 0.7791493212669683
recall_macro: 0.7284511784511783
recall_micro: 0.76
recall_weighted: 0.76
accuracy: 0.76
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Model tree for zkava01/toneconomyroberta
Base model
cardiffnlp/twitter-roberta-base-sentiment