Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use TurqoiseSeagull/autotrain-uad3c-6e4h0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TurqoiseSeagull/autotrain-uad3c-6e4h0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TurqoiseSeagull/autotrain-uad3c-6e4h0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TurqoiseSeagull/autotrain-uad3c-6e4h0") model = AutoModelForSequenceClassification.from_pretrained("TurqoiseSeagull/autotrain-uad3c-6e4h0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 1.7326208353042603
f1_macro: 0.32857142857142857
f1_micro: 0.42857142857142855
f1_weighted: 0.32857142857142857
precision_macro: 0.2857142857142857
precision_micro: 0.42857142857142855
precision_weighted: 0.2857142857142857
recall_macro: 0.42857142857142855
recall_micro: 0.42857142857142855
recall_weighted: 0.42857142857142855
accuracy: 0.42857142857142855
- Downloads last month
- 3
Model tree for TurqoiseSeagull/autotrain-uad3c-6e4h0
Base model
google-bert/bert-base-uncased