Instructions to use mbruton/spa_en_XLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mbruton/spa_en_XLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mbruton/spa_en_XLM-R")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mbruton/spa_en_XLM-R") model = AutoModelForTokenClassification.from_pretrained("mbruton/spa_en_XLM-R") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -29,7 +29,7 @@ Labels are formatted as: r#:tag, where r# links the token to a specific verbal r
|
|
| 29 |
|
| 30 |
- **Developed by:** [Micaella Bruton](mailto:micaellabruton@gmail.com)
|
| 31 |
- **Model type:** Transformers
|
| 32 |
-
- **Language(s) (NLP):** Spanish (es), English (en)
|
| 33 |
- **License:** Apache 2.0
|
| 34 |
- **Finetuned from model:** [English pre-trained XLM RoBERTa Base](https://huggingface.co/liaad/srl-en_xlmr-base)
|
| 35 |
|
|
|
|
| 29 |
|
| 30 |
- **Developed by:** [Micaella Bruton](mailto:micaellabruton@gmail.com)
|
| 31 |
- **Model type:** Transformers
|
| 32 |
+
- **Language(s) (NLP):** Spanish (es), English (en)
|
| 33 |
- **License:** Apache 2.0
|
| 34 |
- **Finetuned from model:** [English pre-trained XLM RoBERTa Base](https://huggingface.co/liaad/srl-en_xlmr-base)
|
| 35 |
|