Instructions to use Nargizi/screeve-postagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nargizi/screeve-postagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Nargizi/screeve-postagger")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Nargizi/screeve-postagger") model = AutoModelForTokenClassification.from_pretrained("Nargizi/screeve-postagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Nargizi/screeve-postagger: direct link, hf CLI and curl.
- Browser
- Download file 382 Bytes
-
https://huggingface.co/Nargizi/screeve-postagger/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Nargizi/screeve-postagger/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Nargizi/screeve-postagger/resolve/main/tokenizer_config.json
382 Bytes
| { | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "<CLS>", | |
| "do_lower_case": true, | |
| "mask_token": "<MASK>", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 200, | |
| "pad_token": "<PAD>", | |
| "sep_token": "<SEP>", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "<UNK>" | |
| } | |