Text Classification
Transformers
TensorBoard
Safetensors
distilbert
Trained with AutoTrain
text-embeddings-inference
Instructions to use Nihardip/hotel-classifier-1000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nihardip/hotel-classifier-1000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nihardip/hotel-classifier-1000")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nihardip/hotel-classifier-1000") model = AutoModelForSequenceClassification.from_pretrained("Nihardip/hotel-classifier-1000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.5429056286811829
f1_macro: 0.5418037563082045
f1_micro: 0.85
f1_weighted: 0.8221169163935278
precision_macro: 0.620127954641229
precision_micro: 0.85
precision_weighted: 0.818425851434701
recall_macro: 0.530115635997989
recall_micro: 0.85
recall_weighted: 0.85
accuracy: 0.85
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Model tree for Nihardip/hotel-classifier-1000
Base model
distilbert/distilbert-base-uncased