Instructions to use sunitha/output_files with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sunitha/output_files 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="sunitha/output_files")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sunitha/output_files") model = AutoModelForQuestionAnswering.from_pretrained("sunitha/output_files", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from sunitha/output_files: direct link, hf CLI and curl.
- Browser
- Download file 320 Bytes
-
https://huggingface.co/sunitha/output_files/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://sunitha/output_files/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/sunitha/output_files/resolve/main/tokenizer_config.json
320 Bytes
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-cased", "tokenizer_class": "BertTokenizer"} |