Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use devipriyarepalli/sentimental-analysis-for-airlines with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devipriyarepalli/sentimental-analysis-for-airlines with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="devipriyarepalli/sentimental-analysis-for-airlines")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("devipriyarepalli/sentimental-analysis-for-airlines") model = AutoModelForSequenceClassification.from_pretrained("devipriyarepalli/sentimental-analysis-for-airlines", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.604269802570343
f1_macro: 0.751387582157275
f1_micro: 0.7533333333333333
f1_weighted: 0.7513875821572747
precision_macro: 0.7503465810250235
precision_micro: 0.7533333333333333
precision_weighted: 0.7503465810250234
recall_macro: 0.7533333333333333
recall_micro: 0.7533333333333333
recall_weighted: 0.7533333333333333
accuracy: 0.7533333333333333
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Model tree for devipriyarepalli/sentimental-analysis-for-airlines
Base model
google-bert/bert-base-uncased