Text Classification
Transformers
Safetensors
bert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use mateiaassAI/teacher_laroseda with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mateiaassAI/teacher_laroseda with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mateiaassAI/teacher_laroseda")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mateiaassAI/teacher_laroseda") model = AutoModelForSequenceClassification.from_pretrained("mateiaassAI/teacher_laroseda", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74d7a1ee4761dd0c878fab72d3040392c71295106ce3d0bd73422ae8a8395ade
- Size of remote file:
- 5.18 kB
- SHA256:
- 6f624c9b23cf5ec02c13ebf01d927c709264dcc7261413aa6e73a382b9401c96
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