Text Classification
Transformers
TensorBoard
Safetensors
roberta
Trained with AutoTrain
text-embeddings-inference
Instructions to use igiag/email-classifier-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use igiag/email-classifier-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="igiag/email-classifier-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("igiag/email-classifier-large") model = AutoModelForSequenceClassification.from_pretrained("igiag/email-classifier-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.727709949016571
f1_macro: 0.5042735042735043
f1_micro: 0.7647058823529411
f1_weighted: 0.7149321266968326
precision_macro: 0.48412698412698413
precision_micro: 0.7647058823529411
precision_weighted: 0.6722689075630253
recall_macro: 0.5277777777777778
recall_micro: 0.7647058823529411
recall_weighted: 0.7647058823529411
accuracy: 0.7647058823529411
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Model tree for igiag/email-classifier-large
Base model
FacebookAI/roberta-large