Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use tynk/autotrain-step2-muc1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tynk/autotrain-step2-muc1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tynk/autotrain-step2-muc1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tynk/autotrain-step2-muc1") model = AutoModelForSequenceClassification.from_pretrained("tynk/autotrain-step2-muc1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 2.3874669075012207
f1_macro: 0.09519230769230769
f1_micro: 0.4125
f1_weighted: 0.2589423076923077
precision_macro: 0.07233796296296295
precision_micro: 0.4125
precision_weighted: 0.19340277777777776
recall_macro: 0.14583333333333334
recall_micro: 0.4125
recall_weighted: 0.4125
accuracy: 0.4125
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Model tree for tynk/autotrain-step2-muc1
Base model
google-bert/bert-base-multilingual-cased