Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use fmesa/mi-modelo-bacan-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fmesa/mi-modelo-bacan-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fmesa/mi-modelo-bacan-test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fmesa/mi-modelo-bacan-test") model = AutoModelForSequenceClassification.from_pretrained("fmesa/mi-modelo-bacan-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa2ac2fd2313430a012be7165bb601ca834794e46f8bf6437e7b62b3535c4dbb
- Size of remote file:
- 268 MB
- SHA256:
- 4007fa213671ad125f449a267c7339322a53376f1beb13475856d10eef726e8b
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