Instructions to use HiTZ/mt-hitz-ca-eu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiTZ/mt-hitz-ca-eu with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("HiTZ/mt-hitz-ca-eu") model = AutoModelForSeq2SeqLM.from_pretrained("HiTZ/mt-hitz-ca-eu") - Notebooks
- Google Colab
- Kaggle
Update README.md
#1
by olatzEHU - opened
README.md
CHANGED
|
@@ -65,7 +65,7 @@ The Catalan-Basque data collected from the web was a combination of the followin
|
|
| 65 |
| Ubuntu | 2,752 |
|
| 66 |
| **Total** | **1.531.980** |
|
| 67 |
|
| 68 |
-
The 9,692,996 sentence pairs of synthetic parallel data were created by translating a compendium of ES-EU parallel corpora into Catalan using the [ES-CA translator from the Aina project](https://huggingface.proxy.nlp.skieer.com/projecte-aina/aina-translator-
|
| 69 |
|
| 70 |
### Training Procedure
|
| 71 |
|
|
|
|
| 65 |
| Ubuntu | 2,752 |
|
| 66 |
| **Total** | **1.531.980** |
|
| 67 |
|
| 68 |
+
The 9,692,996 sentence pairs of synthetic parallel data were created by translating a compendium of ES-EU parallel corpora into Catalan using the [ES-CA translator from the Aina project](https://huggingface.proxy.nlp.skieer.com/projecte-aina/aina-translator-es-ca).
|
| 69 |
|
| 70 |
### Training Procedure
|
| 71 |
|