Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use ManapragadaP/pallavizproject with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManapragadaP/pallavizproject with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ManapragadaP/pallavizproject")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ManapragadaP/pallavizproject") model = AutoModelForSequenceClassification.from_pretrained("ManapragadaP/pallavizproject", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.6612459421157837
f1_macro: 0.7402192648414639
f1_micro: 0.74
f1_weighted: 0.7402192648414639
precision_macro: 0.742556281771968
precision_micro: 0.74
precision_weighted: 0.7425562817719681
recall_macro: 0.7399999999999999
recall_micro: 0.74
recall_weighted: 0.74
accuracy: 0.74
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Model tree for ManapragadaP/pallavizproject
Base model
google-bert/bert-base-uncased