Instructions to use AdapterHub/bert-base-multilingual-uncased-hinglish-sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use AdapterHub/bert-base-multilingual-uncased-hinglish-sentiment with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-multilingual-uncased") model.load_adapter("AdapterHub/bert-base-multilingual-uncased-hinglish-sentiment", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download head_config.json from AdapterHub/bert-base-multilingual-uncased-hinglish-sentiment: direct link, hf CLI and curl.
- Browser
- Download file 458 Bytes
-
https://huggingface.co/AdapterHub/bert-base-multilingual-uncased-hinglish-sentiment/resolve/main/head_config.json
- Command line
-
hf download hf://AdapterHub/bert-base-multilingual-uncased-hinglish-sentiment/head_config.json
-
curl -L -o head_config.json https://huggingface.co/AdapterHub/bert-base-multilingual-uncased-hinglish-sentiment/resolve/main/head_config.json
458 Bytes
| { | |
| "config": { | |
| "activation_function": "tanh", | |
| "bias": true, | |
| "dropout_prob": null, | |
| "head_type": "classification", | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1, | |
| "LABEL_2": 2 | |
| }, | |
| "layers": 2, | |
| "num_labels": 3, | |
| "use_pooler": false | |
| }, | |
| "hidden_size": 768, | |
| "model_class": "BertAdapterModel", | |
| "model_name": "bert-base-multilingual-uncased", | |
| "model_type": "bert", | |
| "name": "hinglish", | |
| "version": "0.2.0" | |
| } |