Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use autoevaluate/multi-class-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/multi-class-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/multi-class-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/multi-class-classification") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/multi-class-classification") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#46
by librarian-bot - opened
README.md
CHANGED
|
@@ -6,6 +6,7 @@ datasets:
|
|
| 6 |
- emotion
|
| 7 |
metrics:
|
| 8 |
- accuracy
|
|
|
|
| 9 |
---
|
| 10 |
|
| 11 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 6 |
- emotion
|
| 7 |
metrics:
|
| 8 |
- accuracy
|
| 9 |
+
base_model: distilbert-base-uncased
|
| 10 |
---
|
| 11 |
|
| 12 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|