Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use sharathyd/sentimental-analysis-model-autotrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sharathyd/sentimental-analysis-model-autotrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sharathyd/sentimental-analysis-model-autotrain")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sharathyd/sentimental-analysis-model-autotrain") model = AutoModelForSequenceClassification.from_pretrained("sharathyd/sentimental-analysis-model-autotrain") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.6591659784317017
f1_macro: 0.7414081289081289
f1_micro: 0.74
f1_weighted: 0.7414081289081289
precision_macro: 0.7448011615713478
precision_micro: 0.74
precision_weighted: 0.7448011615713479
recall_macro: 0.7399999999999999
recall_micro: 0.74
recall_weighted: 0.74
accuracy: 0.74
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Model tree for sharathyd/sentimental-analysis-model-autotrain
Base model
google-bert/bert-base-uncased