Instructions to use linyi/e3-xbch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linyi/e3-xbch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="linyi/e3-xbch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("linyi/e3-xbch") model = AutoModel.from_pretrained("linyi/e3-xbch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from linyi/e3-xbch: direct link, hf CLI and curl.
- Browser
- Download file 690 Bytes
-
https://huggingface.co/linyi/e3-xbch/resolve/main/config.json
- Command line
-
hf download hf://linyi/e3-xbch/config.json
-
curl -L -o config.json https://huggingface.co/linyi/e3-xbch/resolve/main/config.json
690 Bytes
| { | |
| "_name_or_path": "nghuyong/ernie-3.0-xbase-zh", | |
| "architectures": [ | |
| "ErnieModel" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 2048, | |
| "model_type": "ernie", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 20, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "task_type_vocab_size": 16, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.34.0", | |
| "type_vocab_size": 4, | |
| "use_cache": true, | |
| "use_task_id": true, | |
| "vocab_size": 40000 | |
| } | |