Text Classification
Transformers
ONNX
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use albertmartinez/distilbert-multilingual-sdg-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use albertmartinez/distilbert-multilingual-sdg-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="albertmartinez/distilbert-multilingual-sdg-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("albertmartinez/distilbert-multilingual-sdg-classification") model = AutoModelForSequenceClassification.from_pretrained("albertmartinez/distilbert-multilingual-sdg-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 5.0, | |
| "eval_f1": 0.7705941970545616, | |
| "eval_loss": 0.8075858354568481, | |
| "eval_runtime": 5.8073, | |
| "eval_samples": 8605, | |
| "eval_samples_per_second": 1481.76, | |
| "eval_steps_per_second": 23.247, | |
| "total_flos": 2.28033311981568e+16, | |
| "train_loss": 1.097573880901124, | |
| "train_runtime": 397.4859, | |
| "train_samples": 34420, | |
| "train_samples_per_second": 432.971, | |
| "train_steps_per_second": 6.768 | |
| } |