Instructions to use Lammem310/multi-model-support-extractive-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lammem310/multi-model-support-extractive-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Lammem310/multi-model-support-extractive-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Lammem310/multi-model-support-extractive-qa") model = AutoModelForQuestionAnswering.from_pretrained("Lammem310/multi-model-support-extractive-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from Lammem310/multi-model-support-extractive-qa: direct link, hf CLI and curl.
- Browser
- Download file 610 Bytes
-
https://huggingface.co/Lammem310/multi-model-support-extractive-qa/resolve/main/config.json
- Command line
-
hf download hf://Lammem310/multi-model-support-extractive-qa/config.json
-
curl -L -o config.json https://huggingface.co/Lammem310/multi-model-support-extractive-qa/resolve/main/config.json
610 Bytes
| { | |
| "activation": "gelu", | |
| "architectures": [ | |
| "DistilBertForQuestionAnswering" | |
| ], | |
| "attention_dropout": 0.1, | |
| "bos_token_id": null, | |
| "dim": 768, | |
| "dropout": 0.1, | |
| "dtype": "float32", | |
| "eos_token_id": null, | |
| "hidden_dim": 3072, | |
| "initializer_range": 0.02, | |
| "max_position_embeddings": 512, | |
| "model_type": "distilbert", | |
| "n_heads": 12, | |
| "n_layers": 6, | |
| "pad_token_id": 0, | |
| "qa_dropout": 0.1, | |
| "seq_classif_dropout": 0.2, | |
| "sinusoidal_pos_embds": false, | |
| "tie_weights_": true, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.16.1", | |
| "use_cache": false, | |
| "vocab_size": 30522 | |
| } | |