Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use kml-technovation/autotrain-model-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kml-technovation/autotrain-model-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kml-technovation/autotrain-model-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kml-technovation/autotrain-model-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("kml-technovation/autotrain-model-sentiment-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.36576786637306213
f1_macro: 0.8479053305594936
f1_micro: 0.8896085341461092
f1_weighted: 0.8886625918238689
precision_macro: 0.8555310794575375
precision_micro: 0.8896085341461092
precision_weighted: 0.8880969758527761
recall_macro: 0.8412236343085775
recall_micro: 0.8896085341461092
recall_weighted: 0.8896085341461092
accuracy: 0.8896085341461092
- Downloads last month
- 8
Model tree for kml-technovation/autotrain-model-sentiment-analysis
Base model
google-bert/bert-base-uncased