Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use meghanaa0801/sentiment-analysis-model-autotrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meghanaa0801/sentiment-analysis-model-autotrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="meghanaa0801/sentiment-analysis-model-autotrain")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("meghanaa0801/sentiment-analysis-model-autotrain") model = AutoModelForSequenceClassification.from_pretrained("meghanaa0801/sentiment-analysis-model-autotrain") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.6136841773986816
f1_macro: 0.7654320987654321
f1_micro: 0.7666666666666667
f1_weighted: 0.7654320987654321
precision_macro: 0.7688655672163919
precision_micro: 0.7666666666666667
precision_weighted: 0.7688655672163919
recall_macro: 0.7666666666666666
recall_micro: 0.7666666666666667
recall_weighted: 0.7666666666666667
accuracy: 0.7666666666666667
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Model tree for meghanaa0801/sentiment-analysis-model-autotrain
Base model
google-bert/bert-base-uncased