Text Classification
Transformers
PyTorch
TensorFlow
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use papluca/xlm-roberta-base-language-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use papluca/xlm-roberta-base-language-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("papluca/xlm-roberta-base-language-detection") model = AutoModelForSequenceClassification.from_pretrained("papluca/xlm-roberta-base-language-detection") - Inference
- Notebooks
- Google Colab
- Kaggle
Add link to Colab notebook
Browse files
README.md
CHANGED
|
@@ -103,7 +103,7 @@ As a baseline to compare `xlm-roberta-base-language-detection` against, we have
|
|
| 103 |
|
| 104 |
## Training procedure
|
| 105 |
|
| 106 |
-
Fine-tuning was done via the `Trainer` API.
|
| 107 |
|
| 108 |
### Training hyperparameters
|
| 109 |
|
|
|
|
| 103 |
|
| 104 |
## Training procedure
|
| 105 |
|
| 106 |
+
Fine-tuning was done via the `Trainer` API. Here is the [Colab notebook](https://colab.research.google.com/drive/15LJTckS6gU3RQOmjLqxVNBmbsBdnUEvl?usp=sharing) with the training code.
|
| 107 |
|
| 108 |
### Training hyperparameters
|
| 109 |
|