Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use RishiSaxena/Project1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RishiSaxena/Project1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RishiSaxena/Project1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RishiSaxena/Project1") model = AutoModelForSequenceClassification.from_pretrained("RishiSaxena/Project1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.642180323600769
f1_macro: 0.7155622027918392
f1_micro: 0.7266666666666667
f1_weighted: 0.7155622027918392
precision_macro: 0.7317588325652841
precision_micro: 0.7266666666666667
precision_weighted: 0.7317588325652842
recall_macro: 0.7266666666666666
recall_micro: 0.7266666666666667
recall_weighted: 0.7266666666666667
accuracy: 0.7266666666666667
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Model tree for RishiSaxena/Project1
Base model
google-bert/bert-base-uncased