Text Classification
Transformers
PyTorch
TensorFlow
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use eleldar/language-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eleldar/language-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eleldar/language-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eleldar/language-detection") model = AutoModelForSequenceClassification.from_pretrained("eleldar/language-detection") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#2
by librarian-bot - opened
README.md
CHANGED
|
@@ -5,6 +5,7 @@ tags:
|
|
| 5 |
metrics:
|
| 6 |
- accuracy
|
| 7 |
- f1
|
|
|
|
| 8 |
model-index:
|
| 9 |
- name: xlm-roberta-base-language-detection
|
| 10 |
results: []
|
|
|
|
| 5 |
metrics:
|
| 6 |
- accuracy
|
| 7 |
- f1
|
| 8 |
+
base_model: xlm-roberta-base
|
| 9 |
model-index:
|
| 10 |
- name: xlm-roberta-base-language-detection
|
| 11 |
results: []
|