Text Classification
Transformers
TensorBoard
Safetensors
bert
Trained with AutoTrain
text-embeddings-inference
Instructions to use winderfeld/olist-sentiment-mistral-distilled-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use winderfeld/olist-sentiment-mistral-distilled-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="winderfeld/olist-sentiment-mistral-distilled-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("winderfeld/olist-sentiment-mistral-distilled-bert") model = AutoModelForSequenceClassification.from_pretrained("winderfeld/olist-sentiment-mistral-distilled-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Trained Using AutoTrain
- Problem type: Text Classification
Validation Metrics
loss: 0.36358848214149475
f1_macro: 0.8279263750519386
f1_micro: 0.8696461824953445
f1_weighted: 0.8625564986542884
precision_macro: 0.8539310281610432
precision_micro: 0.8696461824953445
precision_weighted: 0.8668945163298623
recall_macro: 0.8179236397884518
recall_micro: 0.8696461824953445
recall_weighted: 0.8696461824953445
accuracy: 0.8696461824953445
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Model tree for winderfeld/olist-sentiment-mistral-distilled-bert
Base model
neuralmind/bert-base-portuguese-cased