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:
- 687f13b10b25eff1e13b37dd20c688c90f5cb49ca9f03d2bec95eaa9f681aac9
- Size of remote file:
- 3.06 kB
- SHA256:
- c1a4ca47a5edc07c0c796a28830b307ef593c9348d70f1db0ae3dbb9f9628e99
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