Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use shridula/sentiment-analysis-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shridula/sentiment-analysis-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="shridula/sentiment-analysis-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shridula/sentiment-analysis-model") model = AutoModelForSequenceClassification.from_pretrained("shridula/sentiment-analysis-model") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.6437929272651672
f1_macro: 0.7936362761143737
f1_micro: 0.7933333333333333
f1_weighted: 0.7936362761143737
precision_macro: 0.7971380471380471
precision_micro: 0.7933333333333333
precision_weighted: 0.7971380471380471
recall_macro: 0.7933333333333333
recall_micro: 0.7933333333333333
recall_weighted: 0.7933333333333333
accuracy: 0.7933333333333333
- Downloads last month
- 13
Model tree for shridula/sentiment-analysis-model
Base model
google-bert/bert-base-uncased