Instructions to use catpawdev/QA_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use catpawdev/QA_model with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="catpawdev/QA_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("catpawdev/QA_model") model = AutoModelForQuestionAnswering.from_pretrained("catpawdev/QA_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from catpawdev/QA_model: direct link, hf CLI and curl.
- Browser
- Download file 695 Bytes
-
https://huggingface.co/catpawdev/QA_model/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://catpawdev/QA_model/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/catpawdev/QA_model/resolve/main/special_tokens_map.json
695 Bytes
| { | |
| "cls_token": { | |
| "content": "[CLS]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "mask_token": { | |
| "content": "[MASK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": { | |
| "content": "[PAD]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "sep_token": { | |
| "content": "[SEP]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "unk_token": { | |
| "content": "[UNK]", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |