Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use VinNik321/airline-sentiment-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VinNik321/airline-sentiment-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VinNik321/airline-sentiment-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VinNik321/airline-sentiment-model") model = AutoModelForSequenceClassification.from_pretrained("VinNik321/airline-sentiment-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.6517418622970581
f1_macro: 0.7322933771209633
f1_micro: 0.74
f1_weighted: 0.7322933771209634
precision_macro: 0.738227569262052
precision_micro: 0.74
precision_weighted: 0.738227569262052
recall_macro: 0.7400000000000001
recall_micro: 0.74
recall_weighted: 0.74
accuracy: 0.74
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Model tree for VinNik321/airline-sentiment-model
Base model
google-bert/bert-base-uncased