Instructions to use Labib11/PMC_GIST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Labib11/PMC_GIST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Labib11/PMC_GIST")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Labib11/PMC_GIST") model = AutoModel.from_pretrained("Labib11/PMC_GIST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from Labib11/PMC_GIST: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/Labib11/PMC_GIST/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Labib11/PMC_GIST/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Labib11/PMC_GIST/resolve/main/1_Pooling/config.json
297 Bytes
| { | |
| "word_embedding_dimension": 1024, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": false, | |
| "include_prompt": true | |
| } |