Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use SriramNatesan/Sriramautotrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SriramNatesan/Sriramautotrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SriramNatesan/Sriramautotrain")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SriramNatesan/Sriramautotrain") model = AutoModelForSequenceClassification.from_pretrained("SriramNatesan/Sriramautotrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.552212655544281
f1_macro: 0.7736493649364936
f1_micro: 0.7733333333333333
f1_weighted: 0.7736493649364936
precision_macro: 0.7740642923836201
precision_micro: 0.7733333333333333
precision_weighted: 0.7740642923836201
recall_macro: 0.7733333333333333
recall_micro: 0.7733333333333333
recall_weighted: 0.7733333333333333
accuracy: 0.7733333333333333
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Model tree for SriramNatesan/Sriramautotrain
Base model
google-bert/bert-base-uncased