Instructions to use remunds/MiniLM_NaturalQuestions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use remunds/MiniLM_NaturalQuestions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="remunds/MiniLM_NaturalQuestions")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("remunds/MiniLM_NaturalQuestions") model = AutoModelForQuestionAnswering.from_pretrained("remunds/MiniLM_NaturalQuestions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from remunds/MiniLM_NaturalQuestions: direct link, hf CLI and curl.
- Browser
- Download file 659 Bytes
-
https://huggingface.co/remunds/MiniLM_NaturalQuestions/resolve/main/config.json
- Command line
-
hf download hf://remunds/MiniLM_NaturalQuestions/config.json
-
curl -L -o config.json https://huggingface.co/remunds/MiniLM_NaturalQuestions/resolve/main/config.json
659 Bytes
| { | |
| "_name_or_path": "microsoft/MiniLM-L12-H384-uncased", | |
| "architectures": [ | |
| "BertForQuestionAnswering" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 384, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1536, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.27.0.dev0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |