Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use yograjm/sentiment-analysis-model-autotrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yograjm/sentiment-analysis-model-autotrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="yograjm/sentiment-analysis-model-autotrain")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("yograjm/sentiment-analysis-model-autotrain") model = AutoModelForSequenceClassification.from_pretrained("yograjm/sentiment-analysis-model-autotrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.5898693203926086
f1_macro: 0.7641792860183664
f1_micro: 0.7666666666666667
f1_weighted: 0.7641792860183665
precision_macro: 0.7764579626648592
precision_micro: 0.7666666666666667
precision_weighted: 0.7764579626648592
recall_macro: 0.7666666666666666
recall_micro: 0.7666666666666667
recall_weighted: 0.7666666666666667
accuracy: 0.7666666666666667
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Model tree for yograjm/sentiment-analysis-model-autotrain
Base model
google-bert/bert-base-uncased