Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use raghu552/airkine-sentiment-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raghu552/airkine-sentiment-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raghu552/airkine-sentiment-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raghu552/airkine-sentiment-bert") model = AutoModelForSequenceClassification.from_pretrained("raghu552/airkine-sentiment-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.6665892601013184
f1_macro: 0.7459654217418192
f1_micro: 0.7466666666666667
f1_weighted: 0.7459654217418191
precision_macro: 0.7537698412698414
precision_micro: 0.7466666666666667
precision_weighted: 0.7537698412698414
recall_macro: 0.7466666666666667
recall_micro: 0.7466666666666667
recall_weighted: 0.7466666666666667
accuracy: 0.7466666666666667
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Model tree for raghu552/airkine-sentiment-bert
Base model
google-bert/bert-base-uncased