Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use modelbyswetha/autotrain-a88ri-7xcnj with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modelbyswetha/autotrain-a88ri-7xcnj with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="modelbyswetha/autotrain-a88ri-7xcnj")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("modelbyswetha/autotrain-a88ri-7xcnj") model = AutoModelForSequenceClassification.from_pretrained("modelbyswetha/autotrain-a88ri-7xcnj", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.7218424081802368
f1_macro: 0.5054421768707483
f1_micro: 0.5714285714285714
f1_weighted: 0.5054421768707483
precision_macro: 0.5095238095238095
precision_micro: 0.5714285714285714
precision_weighted: 0.5095238095238095
recall_macro: 0.5714285714285714
recall_micro: 0.5714285714285714
recall_weighted: 0.5714285714285714
accuracy: 0.5714285714285714
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Model tree for modelbyswetha/autotrain-a88ri-7xcnj
Base model
google-bert/bert-base-uncased