Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Divyash05/autotrain-k0fch-idef9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Divyash05/autotrain-k0fch-idef9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Divyash05/autotrain-k0fch-idef9")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Divyash05/autotrain-k0fch-idef9") model = AutoModelForSequenceClassification.from_pretrained("Divyash05/autotrain-k0fch-idef9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.669853687286377
f1_macro: 0.7048303666050336
f1_micro: 0.7133333333333334
f1_weighted: 0.7048303666050335
precision_macro: 0.7130574744246257
precision_micro: 0.7133333333333334
precision_weighted: 0.7130574744246255
recall_macro: 0.7133333333333334
recall_micro: 0.7133333333333334
recall_weighted: 0.7133333333333334
accuracy: 0.7133333333333334
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Model tree for Divyash05/autotrain-k0fch-idef9
Base model
google-bert/bert-base-uncased