Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use patelvipulkumar/autotrain-hf-finetune-text-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patelvipulkumar/autotrain-hf-finetune-text-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="patelvipulkumar/autotrain-hf-finetune-text-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("patelvipulkumar/autotrain-hf-finetune-text-classification") model = AutoModelForSequenceClassification.from_pretrained("patelvipulkumar/autotrain-hf-finetune-text-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.5555917620658875
f1_macro: 0.7769860552070363
f1_micro: 0.78
f1_weighted: 0.7769860552070365
precision_macro: 0.7814481724893346
precision_micro: 0.78
precision_weighted: 0.7814481724893346
recall_macro: 0.7799999999999999
recall_micro: 0.78
recall_weighted: 0.78
accuracy: 0.78
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Model tree for patelvipulkumar/autotrain-hf-finetune-text-classification
Base model
google-bert/bert-base-uncased